Network traffic simulation
Telecommunications systems, like any complex real-world system, contain many different components which constantly interact with one another, resulting in many complex interrelationships being created between them [1]. The analysis of such a system can become extremely difficult, as most standard modelling techniques analyse each component and do not necessarily take into account the relationships which exist between the components within the system [1][2]. Simulation is an approach which can be used to model large, complex stochastic systems for forecasting or performance measurement purposes [1][2][3].
Simulation is the most common quantitative modelling technique used to analyse systems [1]. The selection of simulation as a modelling tool is usually triggered by the fact that it is less restrictive than other modelling techniques, due to the fact that other methodologies usually impose material mathematical restrictions on the process, and also by their very nature require multiple assumptions to be made, which can restrict the outcome of the analysis [2].
Simulation in Teletraffic Engineering usually contains the following four steps [1][2]:
- Modelling the system as a dynamic [stochastic process]
- Generation of the realizations of this stochastic process
- Measurement of Simulation data
- Analysis of output data
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Simulation methods
There are generally two kinds of Simulations used to model telecommunications networks, viz. Discrete and Continuous Simulations [1][2][4]. Discrete Simulations are also known as Discrete Event Simulations, and are event-based dynamic stochastic systems [1][2]. In other words, the system contains a number of states, and is modelled using a set of variables. If the value of a variable changes, this represents an event, and is reflected in a change in the system’s state [1][2]. As the system is dynamic, it is constantly changing, and because it is stochastic, there is an element of randomness in the system. Representation of Discrete Simulations is performed using State Equations that contain all the variables influencing the system [1].
Continuous Simulations also contain state variables; these however change continuously with time [1]. Continuous Simulations are usually modelled using differential equations that track the state of the system with reference to time [2].
Advantages of simulation
- Normal analytical techniques make use of extensive mathematical models which require assumptions and restrictions to be placed on the model. This can result in an avoidable inaccuracy in the output data. Simulations avoid placing restrictions on the system and also take random processes into account; in fact in some cases simulation is the only practical modelling technique applicable [1][2];
- Analysts can study the relationships between components in detail and can simulate the projected consequences of multiple design options before having to implement the outcome in the real-world [1][2].
- It is possible to easily compare alternative designs so as to select the optimal system [1].
- The actual process of developing the simulation can itself provide valuable insights into the inner workings of the network which can in turn be used at a later stage [1].
Disadvantages of simulation
- Accurate simulation model development requires extensive resources [1][2].
- The simulation results are only as good as the model and as such are still only estimates / projected outcomes [1].
- Optimisation can only be performed involving a few alternatives as the model is usually developed using a limited number of variables [1][2].
Statistical issues in simulation modelling
Input data
Simulation Models are generated from a set of data taken from a stochastic system. It is necessary to check that the data is statistically valid by fitting a statistical distribution and then testing the significance of such a fit. Further, as with any modelling process, the input data’s accuracy must be checked and any outliers must be removed [1].
Output data
When a simulation has been completed, the data needs to be analysed. It is important to remember that the simulation’s output data will only produce a likely estimate of real-world events and should be treated as such. Methods to increase the accuracy of output data include: repeatedly performing simulations and comparing results, dividing events into batches and processing them individually, and checking that the results of simulations conducted in adjacent time periods “connect” to produce a coherent holistic view of the system [1][4].
Random numbers
As most systems involve stochastic processes, simulations frequently make use of random number generators to create input data which approximates the random nature of real-world events. Computer generated [random numbers] are usually not random in the strictest sense, as they are calculated using a set of equations. Such numbers are known as pseudo-random numbers. When making use of pseudo-random numbers the analyst must make certain that the true randomness of the numbers is checked. If the numbers are found not to behave in a sufficiently random fashion, another generation technique must be found [1][4].
Implementation and applications of simulation
Simulations of a telecommunications system are usually implemented in a computer program [2]. The program can be written in a simulation-specific language or in a general-purpose language [1][2]. Simulation-specific languages such as OPNET or GPSS are the most rapid and reliable form of developing and performing a simulation [2][4]. General-purpose languages such as C, C++ or Java are easier to program, but can be tedious for such developments, as well as being prone to errors [2]. When using a general-purpose language, there are two execution methods applicable, viz. the Markov Chain method, which makes use of State Equations, and the Time Trace method, which creates a flowchart of possible events over time [4].
There are many applications that can make use of simulation as an analytical tool [1]. While simulation does require extensive resources, it is still a relatively cost-effective method of pre-testing potential systems. Simulation can also be used to confirm and verify the performance of implemented systems [1].
References
1. ⇧ Flood, J.E. Telecommunications Switching, Traffic and Networks, Chapter 4: Telecommunications Traffic, New York: Prentice-Hall, 1998. 2. ⇧ Penttinen A., Chapter 9 – Simulation, Lecture Notes: S-38.145 - Introduction to Teletraffic Theory, Helsinki University of Technology, Fall 1999. 3. ⇧ Kennedy I. G., Traffic Simulation, School of Electrical and Information Engineering, University of the Witwatersrand, 2003. 4. ⇧ Akimaru H., Kawashima K., Teletraffic – Theory and Applications, Springer-Verlag London, 2nd Edition, 1999, pg 6