networks capacity assignment

Optimization of Computer Networks by Pablo Pavón Mariño

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Chapter 5 Capacity Assignment Problems

5.1 introduction.

$c5-math-0001$

(Slow) Capacity planning : In Internet Service Provider (ISP) backbone networks, network links are virtual circuits hired to a network carrier,or transport connections established in an own network infrastructure. The link costs depend on the link distance and capacity according to the carrier tariffs or the infrastructure cost structure. ISPs periodically (e.g., every 6 months) execute a so-called capacity planning process [1] or capacity expansion ...

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Network flow problem

Author: Aaron Wheeler, Chang Wei, Cagla Deniz Bahadir, Ruobing Shui, Ziqiu Zhang (ChemE 6800 Fall 2020)

1 Introduction
2.1.1 The Assignment Problem
2.1.2 The Transportation Problem
2.1.3 The Shortest-Path Problem
2.1.4 Maximal Flow Problem
2.2.1 Ford–Fulkerson Algorithm
3.1 Formulation of the Problem
3.2 Solution of the Problem
4.1 The Minimum Cost Flow Problem
4.2 The Assignment Problem
4.3 The Shortest Path Problem
5 Conclusion
6 References

Introduction

Network flow problems arise in several key instances and applications within society and have become fundamental problems within computer science, operations research, applied mathematics, and engineering. Developments in the approach to tackle these problems resulted in algorithms that became the chief instruments for solving problems related to large-scale systems and industrial logistics. Spurred by early developments in linear programming, the methods for addressing these extensive problems date back several decades and they evolved over time as the use of digital computing became increasingly prevalent in industrial processes. Historically, the first instance of an algorithmic development for the network flow problem came in 1956, with the network simplex method formulated by George Dantzig. [1] A variation of the simplex algorithm that revolutionized linear programming, this method leveraged the combinatorial structure inherent to these types of problems and demonstrated incredibly high accuracy. [2] This method and its variations would go on to define the embodiment of the algorithms and models for the various and distinct network flow problems discussed here.

Theory, Methodology, and Algorithms

$X$

Additional constraints of the network flow optimization model place limits on the solution and vary significantly based on the specific type of problem being solved. Historically, the classic network flow problems are considered to be the maximum flow problem and the minimum-cost circulation problem, the assignment problem, bipartite matching problem, transportation problem, and the transshipment problem. [2] The approach to these problems become quite specific based upon the problem’s objective function but can be generalized by the following iterative approach: 1. determining the initial basic feasible solution; 2. checking the optimality conditions (i.e. whether the problem is infeasible, unbounded over the feasible region, optimal solution has been found, etc.); and 3. constructing an improved basic feasible solution if the optimal solution has not been determined. [3]

General Applications

The assignment problem.

Various real-life instances of assignment problems exist for optimization, such as assigning a group of people to different tasks, events to halls with different capacities, rewards to a team of contributors, and vacation days to workers. All together, the assignment problem is a bipartite matching problem in the kernel. [3] In a classical setting, two types of objects of equal amount are bijective (i.e. they have one-to-one matching), and this tight constraint ensures a perfect matching. The objective is to minimize the cost or maximize the profit of matching, since different items of two types have distinct affinity.

Table 1. Table of preference
Benefits	Task 1	Task 2	Task 3	...	Task n
Person 1	0	3	5	...	2
Person 2	2	1	3	...	6
Person 3	1	4	0	...	3
...	...	...	...	...	...
Person n	0	2	3	...	3

Figure 2 can be viewed as a network. The nodes represent people and tasks, and the edges represent potential assignments between a person and a task. Each task can be completed by any person. However, the person that actually ends up being assigned to the task will be the lone individual who is best suited to complete. In the end, the edges with positive flow values will be the only ones represented in the finalized assignment. [5]

$x_{ij}$

The concise-form formulation of the problem is as follows [3] :

$z=\sum _{i=1}^{n}\sum _{j=1}^{n}c_{ij}x_{ij}$

Subject to:

$\sum _{j=1}^{n}x_{ij}=1~~\forall i\in [1,n]$

The first constraint captures the requirement of assigning each person to a single task. The second constraint indicates that each task must be done by exactly one person. The objective function sums up the overall benefits of all assignments.

To see the analogy between the assignment problem and the network flow, we can describe each person supplying a flow of 1 unit and each task demanding a flow of 1 unit, with the benefits over all “channels” being maximized. [3]

A potential issue lies in the branching of the network, specifically an instance where a person splits its one unit of flow into multiple tasks and the objective remains maximized. This shortcoming is allowed by the laws that govern the network flow model, but are unfeasible in real-life instances. Fortunately, since the network simplex method only involves addition and subtraction of a single edge while transferring the basis, which is served by the spanning tree of the flow graph, if the supply (the number of people here) and the demand (the number of tasks here) in the constraints are integers, the solved variables will be automatically integers even if it is not explicitly stated in the problem. This is called the integrality of the network problem, and it certainly applies to the assignment problem. [6]

The Transportation Problem

People first came up with the transportation problem when distributing troops during World War II. [7] Now, it has become a useful model for solving logistics problems, and the objective is usually to minimize the cost of transportation.

Consider the following scenario:

$M$

Several quantities should be defined to help formulate the frame of the solution:

$S_{i}$

Here, the amount of material being delivered and being consumed is bound to the supply and demand constraints:

$\sum _{j}^{n}x_{ij}\ \leq S_{i}\qquad \forall i\in I=[1,m]$

The objective is to find the minimum cost of transportation, so the cost of each transportation line should be added up, and the total cost should be minimized.

$\sum _{i}^{m}\sum _{j}^{n}x_{ij}\ C_{ij}$

Using the definitions above, the problem can be formulated as such:

$\quad z=\sum _{i}^{m}\sum _{j}^{n}x_{ij}\ C_{ij}$

The problem and the formulation is adapted from Chapter 8 of the book: Applied Mathematical Programming by Bradley, Hax and Magnanti. [3]

The Shortest-Path Problem

The shortest-path problem can be defined as finding the path that yields the shortest total distance between the origin and the destination. Each possible stop is a node and the paths between these nodes are edges incident to these nodes, where the path distance becomes the weight of the edges. In addition to being the most common and straightforward application for finding the shortest path, this model is also used in various applications depending on the definition of nodes and edges. [3] For example, when each node represents a different object and the edge specifies the cost of replacement, the equipment replacement problem is derived. Moreover, when each node represents a different project and the edge specifies the relative priority, the model becomes a project scheduling problem.

A graph of the general shortest-path problem is depicted as Figure 4:

$c_{12}$

The first term of the constraint is the total outflow of the node i, and the second term is the total inflow. So, the formulation above could be seen as one unit of flow being supplied by the origin, one unit of flow being demanded by the destination, and no net inflow or outflow at any intermediate nodes. These constraints mandate a flow of one unit, amounting to the active path, from the origin to the destination. Under this constraint, the objective function minimizes the overall path distance from the origin to the destination.

Similarly, the integrality of the network problem applies here, precluding the unreasonable fractioning. With supply and demand both being integer (one here), the edges can only have integer amount of flow in the result solved by simplex method. [6]

In addition, the point-to-point model above can be further extended to other problems. A number of real life scenarios require visiting multiple places from a single starting point. This “Tree Problem” can be modeled by making small adjustments to the original model. In this case, the source node should supply more units of flow and there will be multiple sink nodes demanding one unit of flow. Overall, the objective and the constraint formulation are similar. [4]

Maximal Flow Problem

This problem describes a situation where the material from a source node is sent to a sink node. The source and sink node are connected through multiple intermediate nodes, and the common optimization goal is to maximize the material sent from the source node to the sink node. [3]

The given structure is a piping system. The water flows into the system from the source node, passing through the intermediate nodes, and flows out from the sink node. There is no limitation on the amount of water that can be used as the input for the source node. Therefore, the sink node can accept an unlimited amount of water coming into it. The arrows denote the valid channel that water can flow through, and each channel has a known flow capacity. What is the maximum flow that the system can take?

For the source and sink node, they have net flow that is non-zero:

${\textstyle m}$

Flow capacity definition is applied to all nodes (including intermediate nodes, the sink, and the source):

$C_{ab}$

The main constraints for this problem are the transport capacity between each node and the material conservation:

$0\leq x_{ab}\leq C_{ab}$

Overall, the net flow out of the source node has to be the same as the net flow into the sink node. This net flow is the amount that should be maximized.

Using the definitions above:

This expression can be further simplified by introducing an imaginary flow from the sink to the source.

By introducing this imaginary flow, the piping system is now closed. The mass conservation constraint now also holds for the source and sink node, so they can be treated as the intermediate nodes. The problem can be rewritten as the following:

$\quad z=x_{vu}$

The problem and the formulation are derived from an example in Chapter 8 of the book: Applied Mathematical Programming by Bradley, Hax and Magnanti. [3]

Ford–Fulkerson Algorithm

A broad range of network flow problems could be reduced to the max-flow problem. The most common way to approach the max-flow problem in polynomial time is the Ford-Fulkerson Algorithm (FFA). FFA is essentially a greedy algorithm and it iteratively finds the augmenting s-t path to increase the value of flow. The pathfinding terminates until there is no s-t path present. Ultimately, the max-flow pattern in the network graph will be returned. [8]

$0\leq f(e)\leq c_{e},\forall e\in E$

Numerical Example and Solution

A Food Distributor Company is farming and collecting vegetables from farmers to later distribute to the grocery stores. The distributor has specific agreements with different third-party companies to mediate the delivery to the grocery stores. In a particular month, the company has 600 ton vegetables to deliver to the grocery store. They have agreements with two third-party transport companies A and B, which have different tariffs for delivering goods between themselves, the distributor, and the grocery store. They also have limits on transport capacity for each path. These delivery points are numbered as shown below, with path 1 being the transport from the Food Distributor Company to the transport company A. The limits and tariffs for each path can be found in the Table 2 below, and the possible transportation connections between the distributor company, the third-party transporters, and the grocery store are shown in the figure below. The distributor companies cannot hold any amount of food, and any incoming food should be delivered to an end point. The distributor company wants to minimize the overall transport cost of shipping 600 tons of vegetables to the grocery store by choosing the optimal path provided by the transport companies. How should the distributor company map out their path and the amount of vegetables carried on each path to minimize cost overall?

Table 2. Product Limits and Tariffs for each Path
	1	2	3	4	5	6
Product limit (ton)	250	450	350	200	300	500
Tariff ($/ton)	10	12.5	5	7.5	10	20

This question is adapted from one of the exercise questions in chapter 8 of the book: Applied Mathematical Programming by Bradley, Hax and Magnanti [3] .

Formulation of the Problem

$x_{1},x_{2},x_{3},...,x_{6}$

The second step is to write down the constraints. The first constraint ensures that the net amount present in the Transport Company A, which is the deliveries received from path 1 and path 2 minus the transport to Transport Company B should be delivered to the grocery store with path 5. The second constraint ensures this for the Transport Company B. The third and fourth constraints are ensuring that the total amount of vegetables shipping from the Food Distributor Company and the total amount of vegetables delivered to the grocery store are both 600 tons. The constraints 5 to 10 depict the upper limits of the amount of vegetables that can be carried on paths 1 to 6. The final constraint depicts that all variables are non-negative.

Solution of the Problem

This problem can be solved using Simplex Algorithm [11] or with the CPLEX Linear Programming solver in GAMS optimization platform. The steps of the solution using the GAMS platform is as follows:

variables x1,x2,x3,x4,x5,x6,z;

obj.. z=e= 10*x1+12.5*x2+5*x3+7.5*x4+10*x5+20*x6;

Overall, there are 10 constraints in this problem. The constraints 1, and 2 are equations for the paths 5 and 6. The amount carried in path 5 can be found by summing the amount of vegetables incoming to Transport Company A from path 1 and path 4, minus the amount of vegetables leaving Transport Company A with path 3. This can be attributed to the restriction that barrs the companies from keeping any vegetables and requires them to eventually deliver all the incoming produce. The equality 1 ensures that this constraint holds for path 5 and equation 2 ensures it for path 6. A sample of these constraints is written below for path 5:

c1.. x5 =e=x1-x3+x4;

Constraint 3 ensures that the sum of vegetables carried in path 1 and path 2 add to the total of 600 tons of vegetables that leave the Food Distributor Company. Likewise, the constraint 4 ensures that the sum amount of food transported in paths 5 and 6 adds up to 600 tons of vegetables that have to be delivered to the grocery store. A sample of these constraints is written below for the total delivery to the grocery store:

c3.. x5+x6=e=600;

Constraints 5 to 10 should ensure that the amount of food transported in each path should not exceed the maximum capacity depicted in the table. A sample of these constraints is written below for the capacity of path 1:

c5.. x1=l=250;

After listing the variables, objective function and the constraints, the final step is to call the CPLEX solver and set the type of the optimization problem as lp (linear programming). In this case the problem will be solved with a Linear Programming algorithm to minimize the objective (cost) function.

The GAMS code yields the results below:

x1 = 250, x2 = 350, x3 = 0, x4 = 50, x5 = 300, x6 = 300, z =16250.

Real Life Applications

Network problems have many applications in all kinds of areas such as transportation, city design, resource management and financial planning. [6]

There are several special cases of network problems, such as the shortest path problem, minimum cost flow problem, assignment problem and transportation problem. [6] Three application cases will be introduced here.

The Minimum Cost Flow Problem

Minimum cost flow problems are pervasive in real life, such as deciding how to allocate temporal quay crane in container terminals, and how to make optimal train schedules on the same railroad line. [12]

R. Dewil and his group use MCNFP to assist traffic enforcement. [13] Police patrol “hot spots”, which are areas where crashes occur frequently on highways. R. Dewil studies a method intended to estimate the optimal route of hot spots. He describes the time it takes to move the detector to a certain position as the cost, and the number of patrol cars from one node to next as the flow, in order to minimize the total cost. [13]

Dung-Ying Lin studies an assignment problem in which he aims to assign freights to ships and arrange transportation paths along the Northern Sea Route in a manner which yields maximum profit. [14] Within this network composed of a ship subnetwork and a cargo subnetwork( shown as Figure 12 and Figure 13), each node corresponds to a port at a specific time and each arc represents the movement of a ship or a cargo. Other types of assignment problems are faculty scheduling, freight assignment, and so on.

The Shortest Path Problem

Shortest path problems are also present in many fields, such as transportation, 5G wireless communication, and implantation of the global dynamic routing scheme. [15][16][17]

Qiang Tu and his group studies the constrained reliable shortest path (CRSP) problem for electric vehicles in the urban transportation network. [15] He describes the reliable travel time of path as the objective item, which is made up of planning travel time of path and the reliability item. The group studies the Chicago sketch network consisting of 933 nodes and 2950 links and the Sioux Falls network consisting of 24 nodes and 76 links. The results show that the travelers’ risk attitudes and properties of electric vehicles in the transportation network can have a great influence on the path choice. [15] The study can contribute to the invention of the city navigation system.

Since its inception, the network flow problem has provided humanity with a straightforward and scalable approach for several large-scale challenges and problems. The Simplex algorithm and other computational optimization platforms have made addressing these problems routine, and have greatly expedited efforts for groups concerned with supply-chain and other distribution processes. The formulation of this problem has had several derivations from its original format, but its overall methodology and approach have remained prevalent in several of society’s industrial and commercial processes, even over half a century later. Classical models such as the assignment, transportation, maximal flow, and shortest path problem configurations have found their way into diverse settings, ranging from streamlining oil distribution networks along the gulf coast to arranging optimal scheduling assignments for college students amidst a global pandemic. All in all, the network flow problem and its monumental impact, have made it a fundamental tool for any group that deals with combinatorial data sets. And with the surge in adoption of data-driven models and applications within virtually all industries, the use of the network flow problem approach will only continue to drive innovation and meet consumer demands for the foreseeable future.

1. Karp, R. M. (2008). George Dantzig’s impact on the theory of computation . Discrete Optimization, 5(2), 174-185.

2. Goldberg, A. V. Tardos, Eva, Tarjan, Robert E. (1989). Network Flow Algorithms, Algorithms and Combinatorics . 9. 101-164.

3. Bradley, S. P. Hax, A. C., & Magnanti, T. L. (1977). Network Models. Applied mathematical programming (p. 259). Reading, MA: Addison-Wesley.

4. Chinneck, J. W. (2006). Practical optimization: a gentle introduction. Systems and Computer Engineering . Carleton University, Ottawa. 11.

5. Roy, B. V. Mason, K.(2005). Formulation and Analysis of Linear Programs, Chapter 5 Network Flows .

6. Vanderbei, R. J. (2020). Linear programming: foundations and extensions (Vol. 285) . Springer Nature.

7. Sobel, J. (2014). Linear Programming Notes VIII: The Transportation Problem .

8. Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2001). "Section 26.2: The Ford–Fulkerson method". Introduction to Algorithms (Second ed.). MIT Press and McGraw–Hill.

9. Jon Kleinberg; Éva Tardos (2006). "Chapter 7: Network Flow". Algorithm Design. Pearson Education.

10. Ford–Fulkerson algorithm . Retrieved December 05, 2020.

11. Hu, G. (2020, November 19). Simplex algorithm . Retrieved November 22, 2020.

12. Altınel, İ. K., Aras, N., Şuvak, Z., & Taşkın, Z. C. (2019). Minimum cost noncrossing flow problem on layered networks . Discrete Applied Mathematics, 261, 2-21.

13. Dewil, R., Vansteenwegen, P., Cattrysse, D., & Van Oudheusden, D. (2015). A minimum cost network flow model for the maximum covering and patrol routing problem . European Journal of Operational Research, 247(1), 27-36.

14. Lin, D. Y., & Chang, Y. T. (2018). Ship routing and freight assignment problem for liner shipping: Application to the Northern Sea Route planning problem . Transportation Research Part E: Logistics and Transportation Review, 110, 47-70.

15. Tu, Q., Cheng, L., Yuan, T., Cheng, Y., & Li, M. (2020). The Constrained Reliable Shortest Path Problem for Electric Vehicles in the Urban Transportation Network . Journal of Cleaner Production, 121130.

16. Guo, Y., Li, S., Jiang, W., Zhang, B., & Ma, Y. (2017). Learning automata-based algorithms for solving the stochastic shortest path routing problems in 5G wireless communication . Physical Communication, 25, 376-385.

17. Haddou, N. B., Ez-Zahraouy, H., & Rachadi, A. (2016). Implantation of the global dynamic routing scheme in scale-free networks under the shortest path strategy . Physics Letters A, 380(33), 2513-2517.

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Network capacity planning tutorial

What is Network Capacity Planning?

Networks evolve with a business. Whether you are planning a new LAN or maintaining an existing one, you need to model the data capacity requirements on the business’s needs. So, your starting point for network capacity planning is to document your existing resources, the networks current performance metrics, and assess potential changes in demand.

If you currently document your network manually, you will need to start off by finding network management software to use as a network capacity planning tool to map the network and gather statistics on its performance.

Contents [ hide ]

Establishing a baseline

Planning for new networks, monitoring tools, logging equipment, map the network, gather capacity usage data over time, estimate new bandwidth demand, basic network capacity planning, capacity refinements, no-cost capacity options, capacity planning decisions, other capacity issues, network capacity planning, planning future requirements.

The basic outline of core capacity planning tasks is very straightforward. You just need to work out the following three factors:

Current system potential capacity
Current capacity utilization
Bandwidth demand of new equipment/software

If you currently run a network, you may scoff at the simplicity of that outline. However, when performing core capacity planning, it really does pay to keep things as simple as possible . In the baseline phase, you need to consider the load on the network links and equipment that will be needed to support the new requirement. For example, if you are adding on 20 new user endpoints to the network, the segment that will connect those new nodes together and links that will carry traffic between those terminals and the servers that hold the storage and software that those users are expected to use are key. Your firewall and load balancer capacity is not relevant in this scenario.

So, capacity planning only needs to concern those resources that will be directly involved in serving the new requirement . You will be expected to add on new applications or equipment throughout your career managing your company’s network. Therefore, it is better to get a baseline for the whole system upfront , so that you are ready to implement any new requirement that the business comes up with. Those system statuses need to be updated regularly so that future capacity planning exercises aren’t based on out-of-date network metrics.

You will need to take into account the following factors when gathering information about your current system:

Network equipment
End-user equipment
On-premises servers
Offsite servers
Outsourced services
Existing applications
Remote access requirements
External traffic demand

Getting your baseline information involves measuring the existing network’s performance . This involves tracking data from both your network devices and your link capacity at the moment. Understanding which business critical applications are delivered to which users is also a benefit because this can help you re-organize subnetworks to reduce pressure on trunk links. You can also reorganize scheduled tasks to reduce demands on important links and free up bandwidth for user-facing applications during business hours.

Creating a network from scratch to support a fully-operational new business with no historic network performance is a tough job. Fortunately, it is also very rare. No business starts up fully formed. New businesses need to perform market research, run trials of products and services and assemble a team. So you will have a period when you can try your new network and check on the performance of each application and business service as you test it on the server. So, you never really will be required to create a new network that will be live on day one . Capacity planning is a recursive process that takes place as the business sets up.

Data gathering on networks is a complicated task that requires views on a wide range of information types that originate from a large number of sources . It is impossible to keep track of all the variables that can impact network performance without an automated monitoring tool . So, during your baselining task, you need to use an efficient monitoring suite to gather all of the information that establishes an acceptable level of service.

Your first task, when establishing a baseline, is to create an inventory of all of your equipment. For network capacity planning you need to focus on your network devices, such as switches and bridges . For later phases in the traffic capacity planning exercise, you will also need to list your end user devices and servers. If you have wifi routers in your network, you will also need to record the data throughput rate of each of them. If you use Software-as-a-Service online (SaaS) or employ cloud storage , you need to include those offsite resources as well.

Some network monitoring tools are able to cover entire WANs , so if you want to monitor many sites as an integrated network, you should be sure to choose a monitor that has multi-site capabilities.

You specifically need to know the total throughput capacity of each piece of network equipment . You should already know the bandwidth capacity of your cable type. It is not unusual for networks to include a mix of cable types. The upstream and downstream bandwidth availability of your internet service and the capacity of interfaces to external networks should also be logged. The throughput capacity of your firewalls, gateways, load balancers, and proxy servers also needs to be recorded.

With this data gathering phase, you fulfill the first information requirement of your capacity planning exercise, which is the potential maximum capacity of your network.

Getting a visual representation of the network is very important. You may have a good equipment inventory tool that allows you to sort data and focus on each piece of equipment. However, there is nothing like a map to enable you to see instantly which switches are serving more links than the others. A visual representation also makes it easier to see how many links a typical conversation has to cross. The network mapping tool that you use should be able to display capacity utilization through color-coding so you can spot congestion instantly.

If your business is web-based, your network traffic analyzer should specialize in tracking demand into your network from external sources . If you rely heavily on cloud-based services, your monitor should be able to track connections to those remote servers. Similarly, your bandwidth analyzer should be able to monitor internet links between sites if you run a WAN.

When you monitor your network, you will get to see which hours of the day, and which days of the month experience peak traffic loads or spikes, and when traffic levels are subdued. During regular operations, you should be able to schedule administrative tasks to run outside of business hours in order to avoid competing with user traffic at peak times. So, before you add on new services, you should have your network working efficiently. When your capacity planning study begins, you need to track the capacity utilization over several working days so that you can get a view of your peak network utilizations over the resources that will serve the new demand.

Get a record of high, low, and average throughput on the links and equipment that are due to work for the new requirements. You need to know whether the peak demand is close to typical or only occurs on a limited period on any given day .

You also need to measure the packet loss rate . You should record this figure as a percentage. See whether the packet loss rate increases during high demand periods. Apply the packet loss rate to your bandwidth availability by deducting the calculated percentage from your figure of available bandwidth. Alternatively, you can choose to add that percentage to the new bandwidth requirements.

Now you have the second input to your capacity planning. That is, the current data throughput of your network.

Getting a figure for bandwidth requirements for any new software that you intend to install is pretty easy. The providers of most software that is to be delivered across a network give information on the bandwidth that their software uses in its Systems Requirements statement. In some cases, this figure is expressed as a range. If that range is wide, the information won’t be much help to you. Usually, the bandwidth requirements express the throughput expected to serve one user , so if you are buying several licenses, you should plan to have the extra capacity to cater to the given bandwidth requirement multiplied by the number of licenses.

If the software company does not provide a bandwidth requirement, ask for a trial copy and run it over the network as a single instance to log its throughput. The commissioning manager in your company who requested the installation of this software will probably require an assessment period. That run through will enable you to gather bandwidth requirement data .

Your monitoring tool should enable you to track the traffic of a specific piece of software . If it doesn’t, you should be able to track protocol activity . If your new software is replacing a package that is already in use on the network, both the new and old software will probably use the same protocol or port number. In that case, you would need to record that traffic outside of normal business hours so that you don’t confuse the bandwidth utilization used by both systems. Another option is to employ QoS tagging so that you can run the new software test while the old system is still in operation. Once you have a standard traffic pattern for one user, multiply that figure by the maximum number of users that will be allowed to use that software simultaneously.

If your capacity planning exercise examines the effects of adding on new user devices to the network, record the amount of router traffic to and from an existing node that serves the same work roles that will use the new devices. Multiply those traffic figures by the number of endpoints that you expect to add to the network.

The figures you acquire with this phase of the study give you the third element of data that the capacity planning task requires — new bandwidth demand.

Your network performance monitor and your bandwidth analyzer should have provided you with the input data for your capacity planning inquiry. Hopefully, you also have a network administration tool that supports analysis functions .

The capacity test is a simple exercise of adding the new bandwidth requirements to your current peak and average bandwidth usage. Next, look at the maximum capacity of each piece of equipment and each network link that the traffic will pass through. You should know this route thanks to your network map. Look at the network element that has the lowest capacity . Don’t forget to adjust your figures for the packet loss rate. If your new demand is below that level, you are all clear.

If your tightest bottleneck can’t cope with the new traffic, look at other elements in the path in order of increasing capacity to see which of those also could not cope with the new traffic demand.

You may find that you have more than one element that needs to be replaced to keep the new service operating at an acceptable speed.

The discovery that your network cannot cope with the new capacity demand means that you need to make some decisions and you will probably need to include others in your decision making . You will need to present options to other managers in the business and so a network administration tool that has reporting functions will really help you in this task.

In any infrastructure capacity decision you always have three options:

Buy new resources.
Reorganize the network to add capacity to the path of this requirement.
Cancel the new acquisition.

The initial capacity test showed you a list of equipment that could not cope with extra bandwidth demand. Get quotes for replacements of each element with new equipment that has greater capacity. This will give you the cost of the first option on the above list: buying new resources . One advantage is that you don’t have to throw your existing equipment away. It can be moved to other parts of the network to provide spare capacity that will cater to future extra demand. Every network administrator should plan for a continual increase in network bandwidth .

You will need more information about your network to support this decision-making process. The no-cost option of reallocating underutilized resources is always going to be the choice of the board of directors , so you first need to investigate that possibility before raising their hopes. You can go in three directions to get the most out of your network:

Physically move network equipment.
Reallocate resources virtually.
Implement traffic shaping.

All of these options require levels of analysis that cannot be achieved without network administration tools.

Reassign physical equipment

Option one on the no-cost options list would be nice, but you probably don’t have a spare switch on your network with links that carry almost no traffic. If you do, then you have probably overspent at some point in the past. The traffic throughput of all of your network devices shown on the network map will aid your decision making when looking for spare capacity. A printout of your network map , showing the capacity and utilization of all your links will add a visual element to your presentation when you need to include non-technical managers in the planning process.

Use your network administration tool’s analysis functions to log the maximum load on each switch and router. If there are two that serve less than half of their maximum capacity and one of them has enough spare sockets to take more cables, then you have the option of merging two segments and freeing up a switch. With that extra equipment, you can divide up the overloaded segment and double your capacity to those endpoints.

This is just one example of the steps you can take to physically expand the capacity on one part of your network at the expense of another in order to cater to new traffic demand. The monitoring capabilities and analytical functions of your network administration tool will get you through this complicated task .

Introduce virtualization

Virtual reallocation is possible if you have virtualization applications, such as Hyper-V or VMWare . Sharing a server among endpoints not only reduces hardware costs, but it can also reduce current network traffic volumes. When users log in to the processor remotely, much of the interfacing between applications and storage can be limited to one server or run over one link. This generates less traffic than messages passing back and forth across the whole network between user endpoints and servers.

Virtualization is complicated and difficult to assess , so you need a tool that is dedicated to monitoring the traffic that these systems create. If you don’t already implement virtualization, it is probably better to put off this option for consideration at a later date when you have less pressure on your time.

Implement traffic shaping

If you don’t have spare capacity anywhere on your network and the new requirement is going to overload part of your system, you could propose traffic shaping . The debate between buying new equipment and traffic shaping is one of the main reasons that you need to get senior staff involved in the decision over how to cater to the new requirement. Being able to distill network capacity information into easily-digestible graphs and summaries will ease the process of trade-off. Someone is not going to be happy about the results of this exercise.

If you tracked your network throughput over time and stored that data, your analysis tool will be able to replay that traffic and segment it by protocol and endpoints. So, you can identify which traffic generating tasks can be moved to overnight processing in order to free up capacity. If you have already used up that option to squeeze extra value out of your network budget, then you still have one more traffic shaping option: prioritization . Try queuing algorithms, such as Class-based Quality of Service (CBQoS) to ensure that some traffic gets through faster than others.

Your capacity planning tool will help you see the effects of this move. However, it ultimately means that some users will get slower response times for the applications that they need to use interactively. You should be able to print off reports from your planning tool to illustrate the effects of this policy.

If the existing services cannot be slowed down to create sufficient bandwidth space for the new requirement, your management team will be faced with the decision of spending money to buy new infrastructure or canceling the new project. These are decisions that are not within the realm of the network administrator. It would help if you prepared all of the information needed to support the decision and provide stakeholders with illustrated scenarios. The reports from your monitoring tool and snapshots generated out of your capacity planning software will enable you to furnish this supporting documentation.

When you intend to expand the services of your network, there are peripheral issues to consider . You may have room on your server for new software, but will your server’s CPU be able to handle all of the additional demand for processing? Will your server’s network card still be able to send out and receive in all of the traffic that your existing programs generate and still have bandwidth for these extra requirements?

If you interface heavily with the internet, your firewalls, load balancers, proxy servers, and DDoS mitigation capacity may need to be upgraded. Does your internet service agreement have throughput limits? Will your service provider be able to cope with the extra internet traffic that your system changes might generate?

The network does not operate in isolation, so there will be other hardware considerations to take into account when you plan to expand . In each case, measuring current resource usage, recording resource capacity limits, and estimating new capacity requirements will all have to be applied in a capacity planning phase before you can start the implementation of your new project.

No business is static and hopefully, the constant change that your organization experiences is all generated by expansion . You need to allow for traffic growth which will occur naturally as the business grows even if your users don’t demand any new software or hardware. As a network administrator, you need to keep an eye on live data showing traffic volumes because they will rise and the alerts that your system raises to notify you of approaching capacity exhaustion will occur more and more frequently until you add on new network resources .

Resource monitoring and capacity planning are two activities that go hand-in-hand. There are many reasons why you may need to run a capacity planning exercise . However, one constant is that you will always need to have very detailed information on your network’s current activities before you can start to plan for the future.

Network Capacity Planning FAQs

How can ai improve network capacity planning.

The Machine Learning discipline of AI is the most helpful advance in capacity planning technology. Machine Learning works on statistical probability and the more source data an ML system has, the better it can predict future capacity needs.

How is network capacity measured?

Network capacity measurements count the number of bits that pass down a link in each second. As volumes are high, the usual unit of measure is Megabits per second (Mbps). Recently, faster speeds have become so great that they are measured in Gigabits per second (Gbps). A Megabit is one million bits and a Gigabit is one billion bits or 1,000 Megabits.

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A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations-i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge's failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach.

Related Papers

Moatamad Mohamed

The robust design in a flow network is one of the most important problems. It is defined as searching the optimal capacity that can be assigned to the nodes such that the network still survived even under the node's failure. This problem is considered NP-hard. So, this study presents a genetic-based algorithm to determine the maximum node capacity for a two-commodity flow network with node failure. I.e., searching the minimum sum of the assigned capacities and the maximum network reliability. The obtained results show that The proposed GA-based algorithm succeeded to solve the robust problem for the two-commodity flow network considering the node's failure.

Younes Hassan

Abdelhamid Daamouche

System reliability is an important performance index for many real-life systems, such logistic information system, electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. In this paper, we investigate components assignment problem for stochastic flow networks subject to two constraints namely total lead-time, and system reliability. A new approach based on random weighted genetic algorithm (RWGA) is proposed for searching an optimal components assignment which leads to maximizing system reliability and minimizing total lead time. The results revealed that an optimal components assignment leads to the maximum reliability and minimum total lead-time using the proposed approach. Keyword Component Assignment Problem, Genetic Algorithm, Lead-Time, System Reliability.

Many real-world networks such as freight, power and long distance transportation networks are represented as multi-source multi-sink stochastic flow network. The objective is to transmit flow successfully between the source and the sink nodes. The reliability of the capacity vector of the assigned components is used an indicator to find the best flow strategy on the network. The Components Assignment Problem (CAP) deals with searching the optimal components to a given network subject to one or more constraints. The CAP in multi-source multi-sink stochastic flow networks with multiple commodities has not yet been discussed, so our paper investigates this scenario to maximize the reliability of the capacity vector subject to an assignment budget. The mathematical formulation of the problem is defined, and a proposed solution based on genetic algorithms is developed consisting of two steps. The first searches the set of components with the minimum cost and the second searches the flow vector of this set of components with maximum reliability. We apply the solution approach to three commonly used examples from the literature with two sets of available components to demonstrate its strong performance.

International Journal of Network Management

Hamed Alazemi

Michael Todinov

A framework for topology optimization of repairable flow networks and reliability networks is presented. The optimization consists of determining the optimal network topology with a maximum transmitted flow achieved within a specified budget for building the network. A method for a topology optimization of reliability networks of safety-critical systems has also been developed. The proposed method solves the important problem related to how to build within a fixed budget the system characterised by the smallest risk of failure. Both methods are based on pruning the full complexity network by using the bound and branch method as a way of exploring possible network topologies. The methods have a superior running time compared to methods based on a full exhaustive search. A method has also been developed for assessing the threshold flow rate reliability of repairable flow networks with complex topology. The method is based on estimating the probability that the output flow will be equa...

Journal of Computer Science

Abdelhamid DAAMOUCHE

Aswan University Journal of Sciences and Technology

Samer Lahoud

Human-centric Computing and Information Sciences

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Top 7 useful network capacity planning best practices

Capacity Planning

Network Capacity Planning is providing the resources a network needs to prevent any effect on business-critical applications. There is no fixed way to tell when action needs to be taken to prevent any issues. Whether you’re assuring that there is enough bandwidth through a service provider or confirming the load on network devices, having accurate insights is essential.

A fundamental feature of network planning is finding how much bandwidth the network requires. Managers need to determine what capacity will be suitable for network growth in the future. Key initiatives require detailed views into bandwidth usage, combined with previous accounts of usage. Network capacity planning also helps with precise budgeting and provisioning.

Modern networks consist of a collection of routers, switches, firewalls, and other network components. While they are all configured to maintain the best possible function and throughput — both inside the peripheries of the internal network and across links to other networks — procedures teams leverage network capacity planning to identify potential flaws, misconfigurations, or other functionality that could affect a network’s availability within an indicated timeframe. From a high-level perspective, network operators employ a network capacity plan to understand some key network metrics:

Classifications of network traffic
The capacity of existing network infrastructure
Network utilization in the network
Existing network traffic volumes for internal networks and external networks

By conducting this type of network surveying, analysts can understand the maximum capability of exciting resources and the influence of adding incremental new resources needed to help future conditions. While capacity planning helps with the design of new network infrastructure , it can also help to determine additional staff or resources that will control and oversee the network.

Key Metrics for Planning Network Capacity

Network processes often set a baseline for network performance, a key question in each process is: What is expected of the network performance when it is running casually? Network engineers speculate the optimal performance metrics, which can then be applied by network management tools. Key metrics include:

Bandwidth — the maximum rate at which the information can be transferred, generally measured in bits/second
Throughput — the exact rate at which the information is transferred
Latency — the delay between the receiver and the sender
Jitter — the difference in packet delay rate at the destination source
Packet loss — measured as a percentage of packets lost vs the packets sent
Error rate — the total amount of corrupted bits of the sent data

When a threshold for a key performance metric is clocked, the icon for a network element can be indicated, and depending on the severity of the event, may also issue a caution.

Key Capacity Planning Solution Requirements

Key needs for capacity planning solutions vary based on the type of organization utilising it. Network operators can instantly take action on insights from capacity planning tools, some basic onboard solutions include:

Built-in link utilization charts
Ability to set the level of detail of the reports
Ability to create entries using customizable performance values
Predictability of timestamps when full utilization of network resources is predicted to happen
Automatically set alert limits based on data
Active reporting of performance metrics
Ability to generate a custom report and analyse on a routine basis
Allocation of key capacity planning metrics

A Few Network Capacity Planning Best Practices

1. Eliminate Bandwidth Complexities and Metrics Document the extent to which sources, destinations, devices, users, applications, protocols, and services are generating traffic. Employ dashboard services that deliver a page to help the complex bandwidth visibility problems and provide metrics regarding both bandwidth and device strain.

2. Cut Costs and Achieve Uninterrupted Services

Calculate and analyze traffic metrics to confirm performance and capacity baselines. After toggling through periodic reports, an accurate projection of future bandwidth consumption is easily attainable. Having an assessment of the future bandwidths with actual data will help against overextending service provider restrictions on sites that may be growing or experiencing excessive bandwidth consumption.

3. Be Aware of Load Balancing Equipment

Another critical element of network capacity planning is CPU/Memory administration. If the CPU on a device is under too much strain it may throttle its performance in return increasing latency or in the worst-case, crash. Thereby, causing a catastrophic chain reaction putting additional load on other devices. Likewise, insufficient memory handling could cause routing functions on a device to fail. Being conscious of increasing CPU/memory usage on devices will help provide insight into the network.

4. Ensure optimal performance delivery by identifying network bottlenecks

All links have congestion issues and have occasional spikes in traffic. network bottleneck policies are essential to ensure traffic spikes/congestion peaks are smoothed out, and additional bandwidth is allocated to critical network traffic. Without proper policies in place, all traffic has identical priority, and it does not apply to your business-critical applications are getting sufficient bandwidth

For example, without a thorough understanding of the type of traffic passing through a network, it is not possible to indicate if threshold parameters for services like VoIP are meeting target levels. Network Analytics will give you the insights that you need to properly plan network capacity and ensure capacity planning.

5. Understand the effect of network bandwidth utilization

Network bandwidth monitoring is invaluable to assist you to understand bandwidth requirements and network utilization. Bandwidth Monitor can monitor traffic, identify traffic trends, mark out traffic patterns, analyze traffic growth and identify applications that use the majority of the bandwidth. This data is delivered in extensive reports that track real-time usage as well as recorded trends of bandwidth usage over time.

The Bandwidth Usage report summarizes bandwidth utilization for a specified group of devices/interfaces over a calculated period. The information can then be screened based on interfaces, hosts, traffic direction and data range.

Additionally, statements like Top Protocols and Top Applications can also show recorded/real-time bandwidth usage. Network bottleneck policies can also be monitored through classification based reports, which offer a comprehensive view of pre-policy and post-policy traffic side by side, allowing administrators to control targets and determine critical issues like router saturation.

6. Recognize the performance volume of individual components on network infrastructure

Bandwidth Capacity Planning is an active process, anticipating future business requirements and providing the resources for the network to cope when an increase in demand hits. Extra draw on bandwidth can come from both advancement in the type of technology used (e.g. a new VoIP communication system or media streaming service) and an upsurge in the number of end-users (e.g. the following growth into a new market or a merger).

7. Network Redundancy Handling

Redundancy should also be a major concern, and when compared to the cost of unavoidable downtime, redundant connectivity pays for itself numerous times over! When designing redundant connectivity, the best practice is to use an alternate provider, utilizing an alternate link that is geo-separated from the primary connection. Redundancy checking will also require an audit of existing technology. Network flow monitoring tools can be used to locate such tendencies in traffic.

Just as a design engineer uses computer-aided design to create and test the strength of a tower, network engineers can use tools to plan, design and test a complete IT network even before it’s built. Network capacity planning is a crucial aspect of sound network analytics. A healthy network has the growth capacity to meet future needs.

Network capacity planning can provide solutions to new “what if” scenarios such as bandwidth changes due to the deployment of new applications or technologies, changes in traffic, data centre consolidation and migration in multi-vendor networks.

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Network Capacity Assignment for Multicast Services Using Genetic Algorithms

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Capacity and flow assignment of data networks by generalized Benders decomposition

Published: June 2001
Volume 20 , pages 169–189, ( 2001 )

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Philippe Mahey 1 ,
Abdelhamid Benchakroun 2 &
Florence Boyer 3

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A mixed-integer non-linear model is proposed to optimize jointly the assignment of capacities and flows (the CFA problem) in a communication network. Discrete capacities are considered and the cost function combines the installation cost with a measure of the Quality of Service (QoS) of the resulting network for a given traffic. Generalized Benders decomposition induces convex subproblems which are multicommodity flow problems on different topologies with fixed capacities. These are solved by an efficient proximal decomposition method. Numerical tests on small to medium-size networks show the ability of the decomposition approach to obtain global optimal solutions of the CFA problem.

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Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes, CNRS, Campus des Cézeaux, 63173, Aubière, France

Philippe Mahey

Département de Mathématiques et d'Informatique, Université de Sherbrooke, Quebec, Canada

Abdelhamid Benchakroun

France Telecom R&D, Sophia-Antipolis, France

Florence Boyer

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Mahey, P., Benchakroun, A. & Boyer, F. Capacity and flow assignment of data networks by generalized Benders decomposition. Journal of Global Optimization 20 , 169–189 (2001). https://doi.org/10.1023/A:1011280023547

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What Is Network Capacity Planning? – Best Practices for Capacity Management

ParkView Managed Services

If you’re in IT Ops, you know how stressful it can be to meet the expectation of always being ready for tomorrow’s unique demands. Network Admins and Capacity Managers are under immense pressure from leadership to have the optimal amount of server, storage, and networking resources available at all times.

Too many resources, and your organization could lose control of profitability. Too few resources, and your uptime and reputation could take a hit that is hard to recover from.

So, how can an IT leader like yourself keep all the plates spinning? Can a network capacity planning approach provide the optimization solutions you need?

What Is Network Capacity Planning?

Network capacity planning is the process used to identify conditions that could affect your organization’s current and future network performance during a specified period. When used correctly, network capacity planning empowers organizations to predict and overcome bottlenecks, stop performance lags, and sort availability issues within network infrastructure .

As a matter of utility, this process sounds simple. But today’s networks are more complex and diverse than ever and can include vast collections of network devices like routers, switches, firewalls, load-balancers, Web Application Firewalls (WAF), servers, and storage hardware. IT Teams historically have relied on a mix of network capacity planning tools to identify potential shortcomings alongside educated speculation on what’s likely to happen when tech is added.

Are you anxious about finding the right tool for managing your network capacity? Start your initiative off on the right foot – download our network monitoring tool evaluation guide today!

In essence, network capacity planning means reconciling your network traffic volumes, network utilization, types of traffic, and capacity flags with your goals. Interpretation allows network managers to confidently say “yes” or “no” when anticipating resources needed for adoption of new digital tools and apps.

network capacity planning best practices

How ITIL Defines Capacity Management

ITIL, the world-leading framework for IT service management, defines its approach within the service delivery process. It notes that Capacity Management is responsible for ensuring that the capacity of IT services today and in the future so that the IT infrastructure is able to deliver the agreed service level targets in the short, medium, and long-term and in a cost effective and timely manner.

According to the framework, ITIL’s capacity management contains four sub-processes that push users to adhere to network capacity and performance management:

Business Capacity Management

The first sub-process is business capacity management, which allows organizations to take what they have and then to layer on business needs and plans, translating them into actual required capacity and performance needs.

Service Capacity Management

Service capacity management is the second ITIL process. It allows organizations to manage, control and predict the performance and capacity of operational services. Service capacity is usually the most resource consuming process as it includes initiating proactive and reactive actions in modeling, testing and production environments to ensure that performance can meet agreed targets.

Resource Capacity Management

Resource allocation capacity management allows organizations to manage, control and predict the performance, utilization, and capacity of individual IT components.

Capacity Management Reporting

Capacity management reporting provides ongoing management reporting on service and resource capacity, utilization, and network performance.

Capacity Planning vs. Capacity Management

ITIL differentiates between capacity management vs. capacity planning, noting that planning needs to be conducted as an upfront process that reflects current needs. Alternatively, capacity management is ongoing and refers to the entire lifecycle of monitoring.

The repeatable data center capacity planning cycle therefore looks like: – data collection > data analysis > optimizing infrastructure > and back again to monitoring collected data.

Benefits of Capacity Management in IT

Capacity management allows Infrastructure Leads to correctly advise if new digital innovations can be delivered within the existing network infrastructure. Capacity management can then flag likely bottlenecks due to shortcomings in your network.

KPIs for Network Capacity and Performance Management

Establishing baselines is required, but this process can be extremely involved if you take this path without the right tools.

Key metrics need to be captured to establish KPIs for network capacity management. These performance indicators consider all critical network elements and help your organization formalize baselines for performance and availability. These metrics frequently include:

Maximum bandwidth rates (in bits/second)
Actual throughput rates of information transferal
Latency rates – (the delay between the sender and the receiver)
Packet loss (via % of packets lost vs. packets sent)
Error rates (via number of corrupted bits as a % of total sent)

Network Capacity Planning Best Practices

To reach best practice status, there are more elements to baseline than just the KPIs in the previous section! You need to consider all network elements, like network equipment (switches, routers, etc.), end-user equipment, on-prem servers, offsite servers, outsourced services, existing application impact, remote access requirements, VMs and external traffic demands.

Once you have your baselines set, it’s important to identify existing and potential network bottlenecks. If a latency rate is sub-par today, it would be a mistake to make all future capacity decisions without resolving a fundamental issue.

Organizational performance is one thing, but it’s also important to segment the performance of different network infrastructure components . Is older equipment bringing down your average? Using a data center hardware monitoring service could help preserve Uptime while saving you the cost of replacement.

How Do You Determine Server Capacity?

Servers deserve individual attention when it comes to considering existing and forecasted workloads across a given period. In its network capacity planning best practices blog, Microsoft advises that for better server capacity planning, tracking should be conducted of server CPU, disk, network, and likely memory consumption.

Slightly harder to forecast are individual application loads on servers which fluctuate constantly depending on volumes of transactions. For this use case, servers need to facilitate both low levels and peak levels of utilization. For instance, how do you work out a server capacity in a constantly changing retail transacting environment?

A good idea is to track both average and peak load indicators for server details such as CPU utilization percentage levels, server memory utilization percentage levels, and then define usage into cold and hot buckets so the impact of expanding apps can be logged.

Benchmark Current and Forecast Future Capacity

Determining network capacity also requires logging through bandwidth capacity planning. Detail the bandwidth utilization (the capacity of a channel) alongside network latency (the time it takes to travel between one point in a network and another fixed point) and benchmark them to avoid application performance glitches.

The number of devices on the network is another factor to note in WAN capacity management, especially when the latency of a network connection is low. Here bandwidth can quickly become a performance limiting factor if there are too many resources sharing the same network connection. Wireless site surveys and network refreshes can address this issue, but only if your capacity planning is accurately tracking metrics like this.

Choosing the Right IT Infrastructure Capacity Planning Tools

Detailed planning is essential. There are many traditional IT infrastructure capacity planning tools and network capacity modeling tools out there to tap into. Predicting capacity requirements is the hardest part of the capacity puzzle. No business stays static, and resource demand is always in flux. Support tools are available!

The right enterprise network monitoring software can help you discover your full array of network assets , analyze your network flow , and manage network faults and events before your systems fail. All of this is possible with Entuity Software™.

If you’d prefer a more hands off approach, Park Place Technologies offers ParkView Managed Services™, comprehensive infrastructure managed services that can free your team up for creative problem-solving while we cover the fundamentals.

Regardless of your needs, our global data center and networking optimization firm is here to help. Contact us to learn more about how we can support your capacity planning and management efforts today!

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Channel Allocation Strategies in Computer Network

Channel Allocation means to allocate the available channels to the cells in a cellular system. When a user wants to make a call request then by using channel allocation strategies their requests are fulfilled. Channel Allocation Strategies are designed in such a way that there is efficient use of frequencies, time slots and bandwidth.

Types of Channel Allocation Strategies:

These are Fixed, Dynamic, Hybrid Channel Allocation and Borrowing Channel Allocation as explained as following below.

Fixed Channel Allocation (FCA):

Fixed Channel Allocation is a strategy in which fixed number of channels or voice channels are allocated to the cells. Once the channels are allocated to the specific cells then they cannot be changed. In FCA channels are allocated in a manner that maximize Frequency reuse.

Advantages :
Simple to implement and manage
Does not require complex equipment or algorithms
Disadvantages :
Limited channel utilization as unused channels remain unused.
Susceptible to interference and congestion.

Dynamic Channel Allocation (DCA):

Dynamic Channel allocation is a strategy in which channels are not permanently allocated to the cells. When a User makes a call request then Base Station (BS) send that request to the Mobile Station Center (MSC) for the allocation of channels or voice channels. This way the likelihood of blocking calls is reduced. As traffic increases more channels are assigned and vice-versa.

Advantages :
Efficient use of available bandwidth.
Reduces call blocking and improves call quality.
Allows for dynamic allocation of resources.
Requires more complex equipment and algorithms.
May result in call drops or poor quality if resources are not available

Hybrid Channel Allocation (HCA):

Hybrid Channel Allocation is a combination of both Fixed Channel Allocation (FCA) and Dynamic Channel Allocation (DCA). The total number of channels or voice channels are divided into fixed and dynamic set. When a user make a call then first fixed set of channels are utilized but if all the fixed sets are busy then dynamic sets are used. The main purpose of HCA is to work efficiently under heavy traffic and to maintain a minimum S/I.

Provides the benefits of both FCA and DCA.
Allows for dynamic allocation of resources while maintaining predictable call quality and reliability.
Requires more complex equipment and algorithms than FCA.
May not provide the same level of efficiency as pure DCA.

Borrowing Channel Allocation (BCA) :

when a cell experiences high traffic demand and all of its channels are occupied, it can borrow channels from neighboring cells that are not being used at that time. The borrowed channels are assigned to the busy cell and are used to support the additional traffic demand. Once the demand subsides, the borrowed channels are released and returned to their home cell. BCA can be implemented manually or automatically using algorithms or policies but the main disadvantage is that if the borrowed channel is reclaimed by the original cell the call drop may occur.

Efficient use of available bandwidth.
Reduces call blocking and improves call quality.
Increases interference between cells.
Can cause call drops if borrowed channels are reclaimed by the home cell.

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Network capacity planning: How to and 10 best practices

Network slowdowns and outages are more than just a nuisance — they can drain valuable time and money from your business.

Many organizations struggle with these issues because they fail to plan for capacity . Without a clear strategy, your network may not handle the growing demands of users and applications, leading to frustration and decreased productivity.

Network capacity planning is a strategic process that helps you anticipate future needs , ensuring your network can handle peak usage and support business growth.

In this article, we’ll dive into the steps and best practices to achieve a resilient and efficient network infrastructure. We’ll cover:

What is network capacity planning?

How to conduct network capacity planning, 10 best practices for network capacity planning, common challenges in network capacity planning, next steps: scale with meter.

Network capacity planning is the process of assessing and predicting the network's bandwidth and performance needs. It generally involves:

Monitoring current usage
Predicting future needs
Allocating resources
Continuous monitoring
Regular adjustments

This process ensures the network can handle current and future demands without slowing down.

Whether your organization needs to add new users and applications or accommodate increased traffic, capacity planning helps avoid network congestion and maintain efficient operations.

Conducting network capacity planning involves a series of steps designed to evaluate current network performance and predict future requirements.

Assess current network performance

The first step in network capacity planning is to assess the current network performance by gathering detailed data on how the network is being used and its overall performance.

Start by using network monitoring tools to collect information on key performance metrics such as:

Bandwidth usage: To understand the amount of data being transferred at any given time. Identify peak usage periods and the types of applications consuming the most bandwidth.
‍ Latency: To assess the delay in data transmission, as high latency can significantly affect user experience.
‍ Packet loss: To pinpoint where data packets fail to reach their destination, as it indicates network reliability issues.

These metrics help you identify existing bottlenecks and areas for improvement. The assessment step forms the foundation for effective capacity planning, helping you make informed decisions about necessary upgrades and optimizations.

Forecast future network needs

To forecast future network needs, analyze business growth and technology trends. Start by reviewing your organization’s growth plans, including staff increases, market expansion, new facilities, and new services.

Consider technology trends like cloud services, video conferencing, and IoT devices that may increase network demand. Understanding these trends helps anticipate additional strain on your network.

Based on this analysis, estimate future bandwidth and resource needs to prevent issues and ensure your network can handle growth without compromising performance.

Identify bottlenecks and limitations

Every network has some issues and limitations. Pinpoint areas where the network is currently constrained or could potentially face constraints in the future to get a better idea of what you need to plan for.

Start by thoroughly analyzing the data collected on current network performance. Patterns that indicate slowdowns or interruptions in service can include:

Frequent high latency spikes during peak usage times.
Consistent packet loss in certain segments of the network.
Bandwidth saturation on specific links or connections.
Recurring downtime or crashes of particular devices.
Slow response times from critical applications.

Use this data to identify which specific devices or applications are causing these bottlenecks. For example, a certain switch or router might be consistently overloaded, or specific applications may be consuming too much bandwidth.

Identifying these problem areas allows you to take targeted actions to address them when developing your capacity plan.

Develop a capacity plan

A capacity plan begins with an outline of the necessary upgrades and expansions needed to meet current and future demands.

Start by creating a detailed list of required improvements based on the assessment and analysis from previous steps. Your capacity plan should include:

Hardware upgrades: Recommend new routers, switches, and increased bandwidth to improve performance.
Software optimizations: Update and configure software for better network management and efficiency.
Infrastructure enhancements: Upgrade cabling and data centers to support more capacity.
Security improvements: Enhance firewalls, encryption, and security protocols to protect the network.
Monitoring and maintenance: Set up tools for real-time monitoring and schedule regular maintenance.
Budget and timeline: Allocate funds and create a timeline for implementing the upgrades.

Focus on the most critical needs first .

Keeping your budget in mind, identify upgrades that will have the greatest impact on network performance. Allocate resources to ensure the most essential improvements are addressed.

Implement changes

Now it’s time to implement the changes outlined in your capacity plan. Begin by:

Carefully planning the implementation to ensure minimal disruption to network services.
Deploying additional resources such as increased bandwidth, new routers, and upgraded switches.
Ensuring seamless integration into the existing network infrastructure.
Reconfiguring network settings to optimize performance and make use of the new resources.

Coordinate the implementation during off-peak hours or scheduled maintenance windows to minimize the impact on users. Communicate with your team and users about any potential disruptions and provide updates on the implementation's progress.

Monitor and adjust

Network capacity planning isn’t a one-time project. It’s an ongoing process to keep your network robust, efficient, and capable of supporting growth and evolving needs.

Begin by setting up tools to continuously track network performance , focusing on key metrics like bandwidth usage, latency, and packet loss.

Review the data collected regularly to ensure the network is performing as expected. Reviews will help you identify any new issues or areas that still need improvement. Be prepared to adjust the capacity plan to address these new challenges and demands.

Implementing best practices in network capacity planning makes for a smooth implementation process. Here are ten essential practices to guide your efforts:

1. Regular monitoring and data collection

Regularly monitoring and collecting performance data is crucial for understanding usage patterns and ensuring network efficiency. It can help you:

Prevent downtime: Helps prevent unexpected network failures.
Perform proactive maintenance: Allows for proactive issue resolution, reducing disruptions.
Support growth: Helps plan for future expansions.

Without regular monitoring, unexpected spikes in traffic can lead to network slowdowns or outages , affecting business operations.

Meter provides a centralized dashboard for real-time insights into network performance to help you quickly detect and resolve issues. Without real-time monitoring, problems can go unnoticed until they cause significant disruptions.

2. Incorporate redundancy

Building redundancy into your network design is essential for preventing single points of failure and maintaining network reliability.

Network redundancy is used to provide alternative paths for traffic flow so that data can keep moving, even if one path fails. Build redundancy into your network design with:

Multiple pathways: Design multiple network paths to ensure data can travel alternative routes if one path fails.
Redundant hardware: Use duplicate routers, switches, and servers to take over in case of hardware failure.
Backup power: Implement uninterruptible power supplies (UPS) and backup generators to keep the network running during power outages.
Failover mechanisms: Set up automatic failover systems that switch to backup hardware or pathways when a failure is detected.

Meter’s network is built with redundancy in mind . We install in any commercial space, providing a complete IT closet/server room buildout with redundant ISP connections .

3. Scalability considerations

Designing your network with scalability in mind ensures it can accommodate growth.

Without scalability, your network may become overwhelmed as demands increase, leading to performance issues and costly upgrades. Implement scalable technologies and solutions like:

Modular hardware that can be added or upgraded as needed to expand capacity.
Cloud services to easily scale storage and processing power based on demand.
Virtualization to efficiently manage and allocate resources.

Scalable solutions also allow for incremental upgrades to avoid large, upfront investments.

Meter’s cloud-based infrastructure offers scalable solutions like modular hardware and switches with virtualization and digital twin capabilities . Watch our webinar to learn more about the Meter Switch Platform and get a sneak peek at upcoming features.

4. Optimize current resources

Optimizing current resources makes the most of your existing network infrastructure to improve performance before considering expensive upgrades.

Start by implementing traffic shaping techniques to manage the flow of data across your network, including:

Bandwidth limiting: Limit bandwidth for non-critical applications to use resources more effectively.
Priority queuing: Assign higher priority to critical data packets so they are transmitted first.
Rate limiting: Control the rate at which data is sent or received to prevent network congestion.

Quality of Service (QoS) policies also help prioritize network traffic so that high-priority services, like voice and video conferencing, get precedence over less critical activities.

Regularly review and adjust your network configurations to reveal areas where adjustments can be made. For example, tweaking settings on routers and switches or redistributing traffic to alleviate congestion.

5. Forecast with accuracy

Forecasting uses reliable data and analysis to anticipate network changes over time. Use accurate forecasting methods like:

Trend analysis: Examine historical data to identify patterns and predict future usage based on past trends.
Regression analysis: Use statistical techniques to model and predict network demand based on factors like user growth or application usage.
Scenario planning: Create different scenarios based on potential business growth, new applications, and changing user behaviors.

Consider business growth projections in your forecasts. Account for factors like increased staff, new office locations, or expansion into new markets. These changes often lead to higher network demands.

User behavior is another crucial factor. Analyze current usage patterns and predict how they might change in the future. For example, more remote work or increased use of video conferencing can drive up network traffic

6. Engage stakeholders

Involve key stakeholders throughout the planning process to align business needs with technical requirements.

Start by identifying the main stakeholders , which typically include IT staff, department heads, and executive leadership. Then make sure to:

Hold regular meetings to discuss current network performance, future needs, and potential upgrades.
Gather input on business objectives, upcoming projects, and expected growth.
Encourage open communication to address any concerns or requirements stakeholders may have.

Collaboration with stakeholders helps align the capacity plan with both operational efficiency and strategic business initiatives.

7. Document and communicate plans

Thoroughly document every step of your capacity planning process, including data analysis, forecasting methods, and identified bottlenecks.

This detailed documentation provides a clear record of your approach and the rationale behind your decisions. Once the plan is documented:

Communicate it clearly to all relevant parties.
Share with key stakeholders, including IT staff, department heads, and executive leadership.
Use straightforward language and visuals to make the information accessible.

Effective communication helps everyone understand the planned changes, timelines, and benefits expected from the capacity plan.

8. Consider security implications

When planning network capacity, ensure security is not compromised. Start by evaluating your current security measures and how they will scale with additional capacity.

Consider potential security risks from expanding the network, such as whether firewalls, intrusion detection systems, and encryption protocols can handle the increased load. Plan for additional security resources if needed, like enhanced monitoring tools and stricter access controls.

Integrate security considerations into every step of your capacity planning process to protect sensitive data and maintain network integrity as it expands.

9. Use automated tools

Leverage automated tools to streamline data collection, analysis, and the planning process.

Automated solutions provide real-time insights into network performance , helping you quickly identify trends and potential issues.

Meter’s solutions can significantly simplify your capacity planning efforts.

Our centralized platform offers advanced features for monitoring, managing, and optimizing network resources. Automating these tasks allows you to save time and reduce the risk of human error.

10. Review and update regularly

Regularly reviewing and updating your capacity plan keeps it relevant.

Conduct periodic reviews to assess how well the current plan meets your network's demands. These reviews help identify any new challenges or areas needing improvement.

As your organization grows and technology evolves, your network requirements will change. Adjust the plan to incorporate these new factors so that it continues to support your operations effectively.

Consistent review and updates keep your network capacity aligned with organizational goals.

Network capacity planning often faces several common challenges that can affect implementation, including:

Solution: Stay informed about industry trends and integrate flexible, scalable solutions that can adapt to new technologies. Regularly review and update your network components to the latest advancements.
Solution : Use forecasting methods that consider growth scenarios and implement scalable technologies. This approach helps maintain network performance even during rapid expansion.
Solution: Prioritize critical upgrades based on their impact on performance and explore cost-effective solutions. Use automated tools for efficient resource management and consider phased implementation to spread out costs over time.

Network scalability and efficiency start with a strong infrastructure.

Meter simplifies network capacity planning with our seamless, cloud-managed Network as a Service.

We provide an end-to-end solution that handles everything from design and installation to ongoing maintenance and support. Meter users enjoy:

Supercharged security: Our centralized platform monitors, manages, and enforces security policies with DNS security, malware protection, VPN capabilities, and real-time insights to prevent unauthorized access and ensure data integrity.
‍ Complete network transparency: Monitor and control your network remotely with our intuitive dashboard, automating configurations and eliminating manual IT intervention.
‍ Improved speed and reliability: Integrated security appliances, routing, and switching ensure seamless network interoperability, high availability with redundancy, and preventive enterprise controls.
‍ Modular hardware and upgrades: Your fee includes all hardware and software. You’ll also get complimentary upgrades and relocation services if you move to a new space.
‍ Automatic failover: We support multiple ISPs for failover. We’ll work with you to determine which configuration is best for your company.

Get in touch for a demo of Meter and see how we can support your network capacity planning efforts.

Special thanks to

for reviewing this post.

Built for network engineers by network engineers

Interview Q

DAA Tutorial

Asymptotic analysis, analysis of sorting, divide and conquer, lower bound theory, sorting in linear time, binary search trees, red black tree, dynamic programming, greedy algorithm, backtracking, shortest path, all-pairs shortest paths, maximum flow, sorting networks, complexity theory, approximation algo, string matching.

Interview Questions

The most obvious flow network problem is the following:

Given a flow network G = (V, E), the maximum flow problem is to find a flow with maximum value.

The multiple source and sink maximum flow problem is similar to the maximum flow problem, except there is a set {s ,s ,s .......s } of sources and a set {t ,t ,t ..........t } of sinks.

Fortunately, this problem is no solid than regular maximum flow. Given multiple sources and sink flow network G, we define a new flow network G' by adding

, add edge (s, s ) with capacity ∞, and ,add edge (t ,t) with capacity ∞

Figure shows a multiple sources and sinks flow network and an equivalent single source and sink flow network

The Residual Network consists of an edge that can admit more net flow. Suppose we have a flow network G = (V, E) with source s and sink t. Let f be a flow in G, and examine a pair of vertices u, v ∈ V. The sum of additional net flow we can push from u to v before exceeding the capacity c (u, v) is the residual capacity of (u, v) given by

(u,v) is greater than the capacity c (u, v).

if c (u, v) = 16 and f (u, v) =16 and f (u, v) = -4, then the residual capacity c (u,v) is 20.

Given a flow network G = (V, E) and a flow f, the residual network of G induced by f is G = (V, E ), where

Given a flow network G = (V, E) and a flow f, an p is a simple path from s to t in the residual networkG . By the solution of the residual network, each edge (u, v) on an augmenting path admits some additional positive net flow from u to v without violating the capacity constraint on the edge.

Let G = (V, E) be a flow network with flow f. The of an augmenting path p is

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UScellular boosts fixed wireless capacity with Tarana technology

UScellular was getting such good uptake of its fixed wireless in a Missouri town, it was running out of capacity and unable to sell more of the product
It decided to work with Tarana to add more capacity to its network
UScellular and Tarana are using CBRS GAA spectrum for the project in Missouri

Even though UScellular plans to sell off big chunks of its spectrum and subscribers to T-Mobile, that's not stopping the regional wireless carrier from pursuing its fixed wireless access (FWA) ambitions — even if that means going head to head with T-Mobile in some regions.

In fact, UScellular is working with Tarana Wireless to increase its FWA capacity in West Plains, Missouri, a town where the demand for FWA is high. So high that the carrier has been getting such good uptake of its fixed wireless that it was about to run out of capacity on its mobile network.

As a solution, the operator decided to tap CBRS General Authorized Access (GAA) spectrum and use Tarana’s technology to add more capacity. UScellular equipped three of its existing towers in the area with Tarana gear.

Back to selling FWA

Where before, UScellular had to stop selling its fixed wireless broadband product in West Plains, its salespeople have now resumed selling the product to more customers.

Mike Irizarry, executive vice president and CTO at UScellular, explained the evolution of the company’s FWA business. He said that UScellular — similar to T-Mobile and Verizon — has been taking advantage of the unused capacity on its 3GPP mobile network.

“When we launched a few years back, it was on our low-band spectrum,” said Irizarry. “Right now, it’s being deployed on our low-band and mid-band licensed spectrum.”

But CBRS GAA spectrum is available to anyone in areas where it’s not in use by the U.S. government (primarily the Navy). So, UScellular decided to tap CBRS spectrum and work with Tarana to deploy the vendor’s FWA equipment on UScellular towers. Tarana’s technology can use either licensed or unlicensed spectrum.

Tarana has more than 200 service provider customers. Tarana CEO Basil Alwan said that at first about 70% of its customers used unlicensed spectrum, while 30% used licensed. But now, it’s about 50/50.

Capacity has been an issue for Verizon and T-Mobile as well. T-Mobile has been especially vocal about not overburdening its mobile network with fixed wireless customers. Irizarry said, “Fixed wireless is going pretty fast. At some point you either have to add capacity or shut off the sales. Once you use up the capacity, using traditional technologies is not economic. We’re excited about the Tarana product, it gives carriers the ability to continue to sell beyond the point when you exhaust traditional capacity.”

It’s not that Tarana is boosting capacity on the 3GPP network. It simply uses the same towers to put up its systems and tap the free CBRS spectrum. Alwan said Tarana’s technology is “really built for fixed wireless.” The company has especially made a name for itself with its proprietary interference cancellation.

“The 3GPP is a great network,” said Irizarry. “But it’s trying to service very different use cases — mobility, fixed wireless and in some cases IoT. Any time you try to meet those requirements, you end up sub-optimizing maximum performance for the use cases.” He said Tarana’s technology is optimized for fixed wireless.

Irizarry said the “initial performance is fantastic.” It’s delivered internet speeds as high as 500 Mbps down and 104 Mbps up.

“Our hope would be we would use it anywhere we run out of capacity,” said Irizarry.

Although Tarana’s technology can work on any spectrum, whether 3GPP or not, its customer premise equipment is proprietary.

Why do this when you're selling the business to T-Mobile?

Irizarry refused to comment on UScellular’s plans in light of the sale of assets to T-Mobile. He would say only, “T-Mobile is buying our assets which include about 30% of our spectrum. The remaining spectrum we will look to monetize. We have not decided what that monetization looks like. We have a business to run. We’re competitors with T-Mobile.”

To dig a bit deeper, Fierce reached out to Recon Analytics analyst Roger Entner, asking why UScellular might be working with Tarana on FWA, given the fact that it wants to sell most of its assets to T-Mobile.

Entner noted that all of UScellular’s customers and most employees will go to T-Mobile, if the deal is approved by the Department of Justice and the FCC. The only thing remaining will be some spectrum and 4,388 towers. He said they’ll essentially be just a tower company.

Entner said that in the interim, “They are still a going concern. Legally speaking, they have to continue operating as if the sale might not happen. If they have contracts that they signed before the T-Mobile deal, they have to continue this. Otherwise, they’re in legal trouble. If UScellular would not compete against T-Mobile that would be a ground to not approve this sale. Only once this sells, then T-Mobile takes over and makes decisions.”

In terms of working with Tarana, if its FWA technology proves to be an effective and economical way to add more capacity, then T-Mobile may be interested in working with Tarana more expansively. “Tarana will work really hard so this performs really well, and then T-Mobile can look at it,” said Entner.

Editor's Note: This story was updated after publication to include comments from Roger Entner.

Telecoms networks and broadband communications

Illwerke vkw to deploy business optical transport solution across Austria

Austrian energy service provider evolving existing network to meet rapidly growing capacity demand and offer 10g, 25g and 100g mobile backhaul and fronthaul services.

Joe O’Halloran, Computer Weekly

Faced with rapidly growing capacity demand, Austrian energy service provider illwerke vkw has tapped Nokia to evolve its existing network to a modern infrastructure based on dense wavelength-division multiplexing (DWDM) and high-performance photonic service engine (PSE) technology capable of increasing network scale, optimising performance and ensuring sustainable growth.

The leading energy service, power utility and telecoms service provider in the Austrian state of Vorarlberg, illwerke vkw generates its power exclusively from renewable sources. It supplies energy to around 200,000 customers and offers telecoms services to mobile network providers (MNOs). The new deployment, which is expected to be completed by late 2024, is intended to provide illwerke vkw with a new, resilient, modernised optical network capable of supporting 400G wavelengths and beyond for business customers.

Featuring Nokia’s 1830 Photonic Service Switch (PSS) , the new optical network will span 15 sites in Vorarlberg providing 10G, 25G and 100G mobile backhaul and fronthaul transport services to connect MNO cell sites and central offices. Illwerke vkw AG will also provide optical transport services for other business customers in the region.

Commenting on the deployment, illwerke vkw head of IT Christoph Märk said: “We are excited to work with Nokia to evolve our existing network infrastructure. The new optical network will enable us to meet the growing capacity demands and provide high-quality telecoms services to mobile service providers and business customers in Vorarlberg.”

Matthieu Bourguignon, senior vice-president and head of Europe for network infrastructure business at Nokia, added: “As a premier energy service provider in Austria, illwerke vkw AG stands out by harnessing renewable energy and extending its network and expertise to offer telecommunications services to mobile network operators.

“Like its global counterparts in the energy and telecoms sectors, our customer is facing unrelenting demand for capacity which requires significant network modernisation. Nokia will provide a resilient and superior optical transport network capable of supporting 400G wavelengths and beyond, which will serve the present and future requirements of illwerke vkw AG’s customers.”

Nokia is confident the new network will allow illwerke vkw to manage wavelengths at the photonic layer efficiently. It will include optical channel protection to enhance resiliency of services and support for all fronthaul protocols, making the network ready to support multi-generation radios (3G/4G/5G). This includes support of high-performance synchronisation distribution (frequency/time/phase) meeting strict (ITU-T G.8273.2 Class B and C) timing requirements for backhaul and fronthaul services as enabled through the 1830 PSS and 1830 Time-sensitive Packet Switch (TPS), respectively.

The new optical network also employs the Nokia WaveSuite Network Operations Centre (WS-NOC), providing unified end-to-end optical management and support functions including service provisioning and service assurance.

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Planning Resources with Networks

After completing this lesson, you will be able to:

Create capacity requirements for internally processed activities .
Distribute the work of internally processed activities .

Resource Planning with Networks

James and manual discuss the planning of project resources.

James is a project planner at Hybrid Machinery. Manuel is responsible for capacity planning and resource and material procurement. They are discussing the management of project resources.

Resource Planning in Projects

A project can contain both internally processed activities and externally processed activities.

You can use activities in a network to plan the resources required for a project.

The SAP Project System (PS) distinguishes between the following types of activities:

Defines the work to be carried out by machines or people. You can evaluate the capacities of the various work centers involved, reschedule these capacities as necessary, and distribute work among employees.

Defines activities that are required from external resources like vendors and subcontractors. External processing is carried out by the purchasing department.

A service activity is also used to procure services externally. It is defined by service specifications and value limits that can be rendered by the service provider. Service processing includes maintenance of service entry sheets. Service processing is carried out by the purchasing department.

Internal Processing with Networks

Processing of internal project network activities.

The process for an internally processed activity is explained: a capacity analysis analyzes capacity requirements, planned dates and planned costs. Capacity leveling comes after, followed by workforce planning. Confirmations then lead to a capacity load reduction and the posting of actual dates and costs.

Internally processed activities are used to plan work that is to be carried out within your company.

You must define additional data for internally processed activities so the system can create capacity requirements and calculate costs for the following:

A work center is the place where an activity is carried out or work output is produced. A work center contains the scheduling and capacity data required for scheduling and capacity planning. A work center also contains data for costing.

Work is the planned output of machines or personnel to complete an activity.

The duration specifies for how long an activity lasts.

The activity type (and the cost center derived from the work center) specify a price for calculating the planned costs of an activity.

You can determine how much of the work center capacity is used for the activities. If necessary, you can use the Project Planning Board to level capacities. When you confirm activities during the project execution phase, you enter actual dates.

Create Capacity Requirements for Internally Processed Activities

Workforce planning, james and manuel discuss the planning of the internal workforce.

James is a project planner at Hybrid Machinery. Manuel is partly responsible for capacity planning. They discuss the required planning activities for the internal workforce.

"After the work has been completed, the work center where the work was executed out is available again for other tasks."

An example of workforce planning is shown.

Once you have created capacity requirements, you can enter workforce planning. Using workforce planning, you can distribute work among employees, that is, you can assign persons to activities. You can also schedule personnel for an activity. Project and work center views are available to help you distribute work among employees. In both views, you can assign persons to activities and access various reference data, such as the availability of a person from Human Capital Management (HCM).

Distribute the Work of Internally Processed Activities

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Party could last until 4 am at exclusive club inside l.a.’s intuit dome, thanks to just-passed legislation, breaking news.

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The first night of the Republican National Convention drew an estimated 18.13 million in the 10 p.m. ET hour across 12 networks. That was up slightly from the 17 million who watched in 2020.

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IMAGES

Network Capacity Assignment v2
(PDF) Capacity assignment in computer communication networks with
Best Network Capacity Planning Tools + Guide
(PDF) Optimizing Capacity Assignment in Multiservice MPLS Networks
PPT
Network Capacity Planning Process Ppt Powerpoint Presentation Outline

COMMENTS

Chapter 5 Capacity Assignment Problems
Chapter 5 Capacity Assignment Problems 5.1 Introduction. Given a network topology , with the set of nodes and the set of links, the capacity assignment problem decides on the capacity allocated to each link .This problem appears in two main (quite diverse) contexts: (i) as a problem to be solved periodically at long time scales (e.g., every 6 months) to upgrade the capacity of deployed links ...
Network flow problem
The network flow problem can be conceptualized as a directed graph which abides by flow capacity and conservation constraints. The vertices in the graph are classified into origins (source ), destinations (sink ... To see the analogy between the assignment problem and the network flow, we can describe each person supplying a flow of 1 unit and ...
PDF Network Flow Problems
Min-Cost Max-Flow. A variant of the max-flow problem. Each edge e has capacity c(e) and cost cost(e) You have to pay cost(e) amount of money per unit flow flowing through e. Problem: find the maximum flow that has the minimum total cost. A lot harder than the regular max-flow. - But there is an easy algorithm that works for small graphs.
Capacity Assignment Problems
This chapter is devoted to capacity assignment problems in two contexts: (i) long‐term capacity planning where link capacities are upgraded, for example every 6 months to match a forecasted traffic growth, (ii) fast capacity allocation in wireless networks, where capacities are updated at a subsecond rate to adapt to varying network conditions by adjusting transmission power or access ...
Capacity Assignment Problems
This chapter is devoted to capacity assignment problems in two contexts: (i) long-term capacity planning where link capacities are upgraded, for example every 6 months to match a forecasted traffic growth, (ii) fast capacity allocation in wireless networks, where capacities are updated at a subsecond rate to adapt to varying network conditions by adjusting transmission power or access ...
PDF Transportation Network Design
Therefore, currently the network designis thought of as supply demand problem or leader-follower game.The system designer leads, taking into account how the user follow. The core of all network design problems is how a user chooses his route of travel. The class of traffic assignment problem tries to model these behaviour.
On Solving the Capacity Assignment Problem Using Continuous ...
Abstract. The Capacity Assignment problem focuses on finding the best possible set of capacities for the links that satisfy the traffic requirements in a prioritized network while minimizing the cost. Apart from the traditional methods for solving this NP-Hard problem, one new method that uses Learning Automata (LA) strategies has been recently ...
Network capacity assignment for multicast services using genetic
Abstract: This letter focuses on the problem of network capacity assignment to accommodate the introduction of additional multicast services in existing unicast networks. The problem is firstly formalized by defining a cost function to evaluate the goodness of a given capacity allocation configuration. Then, a novel approach based on the genetic algorithms is provided to find the near-optimal ...
Channel, capacity, and flow assignment in wireless mesh networks
Capacity assignment (CA), Flow assignment (FA), Capacity and flow assignment (CFA), and Topology, capacity and flow assignment (TCFA) are progressively complex traditional network planning problems that have been studied for wired networks [16]. In the CA problem, the network topology, end-to-end demands, and the flows on different links (i.e ...
Capacity and Flow Assignments in Large Computer Networks
In this paper, the discrete link capacity assignment problem for multiservice networks is considered. The discreteness of the link capacities is forced by the telecommunications equipment ...
PDF Channel assignment strategies for optimal network capacity of IEEE 802
WMN. To take advantage of the increased capacity in such networks, we need to consider several issues: topology for-mation, radio link scheduling, interference mitigation, and routing [2]. The key issue to be addressed in MCMI WMN and the focus of this paper is channel assignment. We con-sider this issue in the context of ieee 802.11s networks.
(PDF) Routing and Capacity Assignment Problem in Computer Networks
The required optimal network capacity assignment will be the one represented by the best individual of all generations. 4.8 The routing algorithm used in the overall SA-based algorithm Given the capacities of all links of the given network topology in the form of a capacity solution, the steps of the algorithm that solves the flow assignment ...
Network Capacity Planning 101: Best Practices & Tutorial
The basic outline of core capacity planning tasks is very straightforward. You just need to work out the following three factors: Current system potential capacity. Current capacity utilization. Bandwidth demand of new equipment/software. If you currently run a network, you may scoff at the simplicity of that outline.
A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network
The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge's failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper.
7 Best Network Capacity Planning Use Cases + Guide
A Few Network Capacity Planning Best Practices. 1. Eliminate Bandwidth Complexities and Metrics Document the extent to which sources, destinations, devices, users, applications, protocols, and services are generating traffic. Employ dashboard services that deliver a page to help the complex bandwidth visibility problems and provide metrics ...
Network Capacity Assignment for Multicast Services Using Genetic
This letter focuses on the problem of network capacity assignment to accommodate the introduction of additional multicast services in existing unicast networks. The problem is firstly formalized ...
Capacity and flow assignment of data networks by generalized Benders
A mixed-integer non-linear model is proposed to optimize jointly the assignment of capacities and flows (the CFA problem) in a communication network. Discrete capacities are considered and the cost function combines the installation cost with a measure of the Quality of Service (QoS) of the resulting network for a given traffic. Generalized Benders decomposition induces convex subproblems ...
Network Capacity Assignment v2
This video shows how PathDesigner assigns the capacity on each network link based on the network topology, the capacity of each site and the traffic paramete...
Channel Assignment Problem
The channel assignment problem between sender and receiver can be easily transformed into Maximum Bipartite Matching (MBP) problem that can be solved by converting it into a flow network. Step 1: Build a Flow Network. There must be a source and sink in a flow network.
What Is Network Capacity Planning?
Network capacity planning is the process used to identify conditions that could affect your organization's current and future network performance during a specified period. When used correctly, network capacity planning empowers organizations to predict and overcome bottlenecks, stop performance lags, and sort availability issues within ...
Channel Allocation Strategies in Computer Network
By capacity of a channel, it means the capacity of the transmission medium (wire or link). Capacity is the number of bits the transmission medium can hold. So basically there are 2 types of channels - Full duplex and half duplex. Half duplex - the transmission can happen in one direction at a time.Full duplex - the transmission can happen in ...
Network capacity planning: How to and 10 best practices
Here are ten essential practices to guide your efforts: 1. Regular monitoring and data collection. Regularly monitoring and collecting performance data is crucial for understanding usage patterns and ensuring network efficiency. It can help you: Prevent downtime: Helps prevent unexpected network failures.
DAA
Let G = (V, E) be a flow network with flow f. The residual capacity of an augmenting path p is. The residual capacity is the maximal amount of flow that can be pushed through the augmenting path. If there is an augmenting path, then each edge on it has a positive capacity. We will use this fact to compute a maximum flow in a flow network.
From Big Cities to Small Towns and Places In Between, T-Mobile
BELLEVUE, Wash. — March 6, 2024 . Light it up! T-Mobile (NASDAQ: TMUS) today announced it's adding new capacity to the country's leading 5G network by activating the 2.5 GHz spectrum it won in auction 108, expanding its Ultra Capacity 5G coverage to new communities and significantly increasing Ultra Capacity 5G bandwidth in many places across the U.S.
Planning Materials with Networks
The BOM transfer function is particularly useful for engineering projects involving BOMs that are created during the course of the project or changed frequently. When the BOM is transferred, assignments are made on a project-specific basis. The project can contain various networks.
USCellular boosts fixed wireless capacity with Tarana technology
It's not that Tarana is boosting capacity on the 3GPP network. It simply uses the same towers to put up its systems and tap the free CBRS spectrum. Alwan said Tarana's technology is "really built for fixed wireless." The company has especially made a name for itself with its proprietary interference cancellation.
Posting Documents to Networks
The figure shows business transactions that establish a connection to work breakdown structure (WBS) elements or activities via an assignment. By assigning the appropriate documents, the commitment or actual costs can be posted directly to a WBS element, network, activity, or activity element.
Illwerke vkw to deploy business optical transport solution across
Austrian energy service provider evolving existing network to meet rapidly growing capacity demand and offer 10G, 25G and 100G mobile backhaul and fronthaul services.
Planning Resources with Networks
A work center is the place where an activity is carried out or work output is produced. A work center contains the scheduling and capacity data required for scheduling and capacity planning. A work center also contains data for costing. Work . Work is the planned output of machines or personnel to complete an activity. Duration
Democratic National Convention Draws 20 Million On First Night
The first night of the DNC on Monday drew 15.32 million 55 and over, 3.51 million in the 35-54 demo and 851,000 aged 18-34, per Nielsen. MSNBC topped the networks, drawing 4.6 million viewers ...