computer science

History of network traffic models

History of network traffic models is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand History of network traffic models rather than just read about it. In short: Design of robust and reliable networks and network services relies on an understanding of the traffic characteristics of the network. Throughout history, different models of network traffic have been developed and used for evaluating existing and proposed networks and services.

Key takeaways

History of network traffic models belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect History of network traffic models to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of History of network traffic models from memory before moving on to harder problems.

Reference excerpt

Design of robust and reliable networks and network services relies on an understanding of the traffic characteristics of the network. Throughout history, different models of network traffic have been developed and used for evaluating existing and proposed networks and services. Demands on computer networks are not entirely predictable. Performance modeling is necessary for deciding the quality of service (QoS) level. Performance models in turn, require accurate traffic models that have the ability to capture the statistical characteristics of the actual traffic on the network. Many traffic models have been developed based on traffic measurement data. If the underlying traffic models do not efficiently capture the characteristics of the actual traffic, the result may be the under-estimation or over-estimation of the performance of the network. This impairs the design of the network. Traffic models are hence, a core component of any performance evaluation of networks and they need to be very accurate. "Teletraffic theory is the application of mathematics to the measurement, modeling, and control of traffic in telecommunications networks. The aim of traffic modeling is to find stochastic processes to represent the behavior of traffic. Working at the Copenhagen Telephone Company in the 1910s, A. K. Erlang famously characterized telephone traffic at the call level by certain probability distributions for arrivals of new calls and their holding times. Erlang applied the traffic models to estimate the telephone switch capacity needed to achieve a given call blocking probability. The Erlang blocking formulas had tremendous practical interest for public carriers because telephone facilities (switching and transmission) involved considerable investments. Over several decades, Erlang's work stimulated the use of queuing theory, and applied probability in general, to engineer the public switched telephone network. Teletraffic theory for packet networks has seen considerable progress in recent decades. Significant advances have been made in long-range dependence, wavelet, and multifractal approaches. At the same time, traffic modeling continues to be challenged by evolving network technologies and new multimedia applications. For example, wireless technologies allow greater mobility of users. Mobility must be an additional consideration for modeling traffic in wireless networks. Traffic modeling is an ongoing process without a real end. Traffic models represent our best current understanding of traffic behavior, but our understanding will change and grow over time."

Network traffic models usage Measurements are useful and necessary for verifying the actual network performance. However, measurements do not have the level of abstraction that makes traffic models useful. Traffic models can be used for hypothetical problem solving whereas traffic measurements only reflect current reality. In probabilistic terms, a traffic trace is a realization of a random process, whereas a traffic model is a random process. Thus, traffic models have universality. A traffic trace gives insight about a particular traffic source, but a traffic model gives insight about all traffic sources of that type. Traffic models have three major uses. One important use of traffic models is to properly dimension network resources for a target level of QoS. It was mentioned earlier that Erlang developed models of voice calls to estimate telephone switch capacity to achieve a target call blocking probability. Similarly, models of packet traffic are needed to estimate the bandwidth and buffer resources to provide acceptable packet delays and packet loss probability. Knowledge of the average traffic rate is not sufficient. It is known from queuing theory that queue lengths increase with the variability of traffic. Hence, an understanding of traffic burstiness or variability is needed to determine sufficient buffer sizes at nodes and link capacities. A second important use of traffic models is to verify network performance under specific traffic controls. For example, given a packet scheduling algorithm, it would be possible to evaluate the network performance resulting from different traffic scenarios. For another example, a popular area of research is new improvements to the TCP congestion avoidance algorithm. It is critical that any algorithm is stable and allows multiple hosts to share bandwidth fairly, while sustaining a high throughput. Effective evaluation of the stability, fairness, and throughput of new algorithms would not be possible without realistic source models. A third important use of traffic models is admission control. In particular, connection oriented networks such as ATM depends on admission control to block new connections to maintain QOS guarantees. A simple admission strategy could be based on the peak rate of a new connection; a new connection is admitted if the available bandwidth is greater than the peak rate. However, that strategy would be overly conservative because a variable bit-rate connection may need significantly less bandwidth than its peak rate. A more sophisticated admission strategy is based on effective bandwidths. The source traffic behavior is translated into an effective bandwidth between the peak rate and average rate, which is the specific amount of bandwidth required to meet a given QoS constraint. The effective bandwidth depends on the variability of the source.

Network traffic models steps Traffic modeling consists of three steps:

(i) selection of one or more models that may provide a good description of the traffic type (ii) estimation of parameters for the selected models (iii) statistical testing for election of one of the considered models and analysis of its suitability to describe the traffic type under analysis. Parameter estimation is based on a set of statistics (e.g. mean, variance, density function or auto covariance function, multifractal characteristics) that are measured or calculated from observed data. The set of statistics used in the inference process depends on the impact they may have in the main performance metrics of interest.

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Worked examples

Example 1 — a first encounter with History of network traffic models

Start with the simplest possible case. Write down what History of network traffic models claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to History of network traffic models before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about History of network traffic models ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of History of network traffic models

In research: History of network traffic models appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses History of network traffic models in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: History of network traffic models is common in secondary-school and first-year university syllabi. It links to neighbouring topics History of telecommunications, Mathematical modeling, Network theory, so understanding it makes those chapters shorter.
In everyday life: Look for History of network traffic models outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study History of network traffic models in 20 minutes

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Frequently asked questions

What is History of network traffic models in simple terms?

Why does History of network traffic models matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study History of network traffic models?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on History of network traffic models.