Teletraffic engineering/How is telephony traffic simulated

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Module by: Nomfundo N. Dlamini

Summary [edit]

Telephone networks are very large and complex networks. This is due to the the technological development and the ever demand for multiple services that these networks have to support. This poses a need to forecast the behaviour of a network should a certain condition occur to it. This can only be done by simulation which is a modelling tool in great use today. It is an economic way, financially and time-wise in representing the system's traffic behaviour by building its model, since computing power to facilitate simulation techniques is accessible. Not only is it accessible, there is also an increasing availability of low cost computer hardware and the development of specialized, easy but powerful and flexible simulation languages which contribute to making simulation a preferred modelling technique. [1]

What is simulation?[edit]

Simualtion can be defined as imitation of a system or enviromnent in order to predict the actual behaviour[2]. In telecommunication systems, it is the most quantitative modelling technique used and its purpose is to model large, complex stochastic systems for forecasting or performance measurement purposes[3]. Simulation is a cost-effective way of pre-testing proposed systems, plans, or policies before incurring the expense of prototypes, field tests, or actual implementations [1]


Network traffic simulation usually follows the following four steps[3] :

  • Modelling the system as a dynamic stochastic (i.e. random) process
  • Generation of the realizations of this stochastic process
  • Measurement of Simulation data
  • Analysis of output data

How is telephony traffic simulated?[edit]

Telephony traffic is simulated by using two simulation methods [1][3] :

  1. Discrete/Discrete event simulations (next event approach)
  2. Continous simulations (fixed time increment approach)


Discrete simulations[edit]

Discrete simulations are due to occurence of an event. The system is dynamic and stochastic. It contains a number of states, modelled using a set of variables. A change in value of variables represents an event and the system's state changes. State equations are used in the representation of discrete simulations. The dynamic nature of the system gives it the characteristic of being in motion, thus constantly changing its position.

When designing a discrete system, there has to be an object and movement of the object through the system. The object may possess attribute values and may interact other certain conditions, leading to an event taking place which in turn changes the state of the system. Since a discrete simulation model may contain contain different objects which in turn lead to different events, a scheduler has to be used. A scheduler performs the complex task of keeping track of all objects and scheduling all events.An example of a discrete simulation model is a queueing system consisting of one server and one queue. Consider customers paying at a supermarkert till being served by the shop attendant. The assumption made is that they stand in a queue and are served by shop attendant at some specifired service time distribution and then leave system.Further, if two events are defined, an arrival and a departure event, also two state variables (the length of the queue and whether the server is idle or busy) we can draw up a possible sequence of events and system state changes [1].

Example 

Table 1: Sequence of events for a simple queueing system (adopted from [1]). File:QueueSystem1.JPG


Continous simulations[edit]

These are usually employed when state variables of the system change continously. There is a mechanism to advance time in fixed increments. Simulation models contain one or more state equations which attempt to model variations in the state of the system over time. The equations are diferential equations that track the system with reference to time.


Exercise[edit]

What type of traffic simulation model would you classify an SMS service? Support your answer.

Implementation and applications of Simulations[edit]

Simulations of a telecommunications system are usually implemented in a computer program [3]. The program can be written in a simulation-specific language or in a general-purpose language[4][5].Simulation-specific languages such as OPNET or GPSS are rapid for development. However, sometimes the flexibility of a general purpose language may be preferred --- a well-known example is the ns-2 simulator ns-2 written in C++. There are many applications that can make use of simulation as an analytical tool. While simulation does require extensive resources, it is still a relatively cost-effective method of pre-testing potential systems.It helps further in confirming and verifying the performance of implemented systems.


Advantages and disadvantages of telephony traffic simulations[edit]

Advantages[edit]

Simulations models come with a lot of advantages which are stated as follows [1].[3]

  • Simulation modelling may be applied to the analysis of a very wide variety of systems. Most complex, real-world systems with significant random elements cannot be accurately described by a mathematical model, which can be evaluated analytically. Simulation is often the only type of investigation possible.
  • Simulation modelling allows the detailed study of the interactions between system components. It allows the manager or analyst to make detailed decisions about the system and then judge the impact and consequences of these decisions. The model may be used for detailed sensitivity analyses to decide which controllable variables have the greatest impact on system performance.
  • Alternative system designs or operating policies can be compared to estimate the system performance. Thus we can choose the best design.
  • Simulation models and the principles of the simulation technique are often easy to understand. It is also easier to convince the non-simulation expert of the validity of the model. The simulation approach encourages active participation by management and operating staff in the modelling exercise.
  • The process of building a simulation model may provide valuable insight and understanding about the system characteristics and how it operates. The value of this advantage is often underestimated.

Disadvantages[edit]

Since simulations are merely a way of predicting the network performance, problems are bound to arise since this is not a real case scenario. What follows are some of the problems that may be encountered when running simulations.

  • Selecting the correct level of detail (or level of abstraction) for a simulation is a difficult problem. Too little detail can produce simulations that are misleading or incorrect, but adding detail requires time to implement, debug, and later change. Furthermore it slows down simulation and can be a distraction from the research problem at hand [6].
  • Running effective simulations comes with experience. This means it might actually take longer to get the correct results which are a through reflection of the network performance.
  • Accurate simulation model development requires extensive resources.

Statistical Issues in Simulation Modelling[edit]

Input Data[edit]

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.[4]

Output Data[edit]

When a simulation has been completed, the data needs to be analysed. The simulation's output data will only produce a likely estimate of real-world events. 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.[4][7]

Random numbers[edit]

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. Random numbers for the simulation are created by a random number generator.

References [edit]

  1. 1.0 1.1 1.2 1.3 1.4 1.5 Kennedy I. G.,Traffic Simulations. Teletraffic Engineering-ELEN7015 Full Lecturing Notes, School of Electrical and Information Engineering, University Of Witwatersrand, Johannesburg,2007.
  2. Wikitionary, Simulation. http://en.wiktionary.org/wiki/simulation, Last accessed: 20 March 2007
  3. 3.0 3.1 3.2 3.3 3.4 Wikipedia, The Free Encyclopedia, Network Traffic Simulation. http://en.wikipedia.org/wiki/Network_traffic_simulation#_note-flood, Last accessed: 20 March 2007.
  4. 4.0 4.1 4.2 Flood, J.E. Telecommunications Switching, Traffic and Networks, Chapter 4: Telecommunications Traffic, New York: Prentice-Hall, 1998
  5. Penttinen A., Chapter 9 – Simulation, Lecture Notes: S-38.145 - Introduction to Teletraffic Theory, Helsinki University of Technology, Fall 1999
  6. Heidemann J., Bulusu N., Elson J., Intanagonwiwat C., Lan K., Xu Y., Ye W., Estrin D., Govindan R.,,Effects of Detail in Wireless Simulations. http://www.isi.edu/~johnh/PAPERS/Heidemann00d.pdf. Last accessed: 21 March 2007
  7. Akimaru H., Kawashima K., Teletraffic – Theory and Applications, Springer-Verlag London, 2nd Edition, 1999, pg 6