Assignment Task:

1 Introduction 

The aim of this assignment is to develop a discrete-event simulation model to examine the operation of a congestion avoidance scheme in a packet switch. Using your simulator, the performance of the scheme (the delay and throughput) will be measured. In EE509 Assignment 2 (given at a later date), approximate measures will be derived using mathematical analysis methods and compared to discrete-event simulation results. 

1.1 The Output Buffered Router (OBR)

Interconnected routers form the backbone of the Internet, allowing IP packets to be transmitted from any source host to destination host. The performance of a router is sensitive to the load placed on its outgoing links. If loading is too high the link is said to become congested and the consequences are long delays for packets passing through the router and/or loss of packets (a reduction in throughput). In this assignment, an event- advance discrete-event simulation model will be developed to investigate the effectiveness of a congestion avoidance algorithm whose goal is to keep link delays low and throughput high, even when a link is overloaded with incoming traffic. The basic function of a packet switch (also referred to as a router or gateway) is to forward packets of data on incoming links to outgoing links, where the outgoing link for each packet is determined by an address contained in the packet’s header. Links operate at a fixed speed (bits per second) which, in combination with the number of bits in the packet (which varies from packet to packet), determines the time required to transmit a packet at an outgoing router port. It is possible that an outgoing port is busy transmitting a packet when another packet, destined for the same port, arrives at a router input port. In this case the newly arriving packet must be stored (queued) until the outgoing port is free. In an output buffered router (Figure 1), queuing is done only at outgoing ports.

The Assignment 

Scenario I - Modelling a Queue with No Loss

The first part of this assignment is to develop simulation code to model a single queue/transmission link, with an unlimited length queue, to measure the system delay and to compare your simulation results to known exact analytical results (the M/M/1 queue detailed in the EE509 lecture notes [4]). The purpose of this exercise is to familiarise you with the fundamentals of random variate transform, event-advance simulation programming and to allow you to validate some basic simulation results against easily-calculated exact mathematical formulae. This simulation will form the basis of the simulators in Scenarios II and III.

Scenario II - Modelling a Queue with a Drop-Tail Policy

The second part of this assignment involves modifying your unlimited length queue to model a limited length queue. Your simulation code will implement the measurement of system throughput and packet delay. This queue will reflect the behaviour of the drop- tail policy in a router and results will serve as a basis for comparison with the RED congestion control scheme, in Scenario III. The specification of the system to be modelled is the same as Scenario I but with a finite queue length of 30 packet buffering spaces. (Note that when a packet enters service it is removed from the head of the queue and stored in a transmission buffer in the output port, immediately starting its transmission. Thus, in total there are 31 storage spaces in the queue/port/link system). 

Scenario III - Modelling a Queue with a RED Congestion Control

In this part of the assignment, you will extend the Scenario II model to implement the RED congestion control scheme and investigate its effectiveness when compared to the simple drop-tail policy. The overall purpose of the exercise is to familiarise you with simulation methods for performance evaluation of complex protocols and to illustrate the role of simulation in network protocol design. You will gain an understanding of how to take a description of a quite complex physical system and reduce it to a model to obtain estimates of pertinent performance measures. (In Assignment 2 (given at a later date), you will also compare simulation results to approximate analytical results and explore where inaccuracies in analytic approximations arise and explore the relative merits of simulation and analysis).

Simple Queue Simulator 

Implement the M/M/1 queuing model simulation as specified in Scenario I. Your simulator should include a batched mean-delay statistic that calculates the mean delay at periodic intervals (see Section 1.6). 

(1.1) Give a brief description of how your simulator generates random numbers with the specified distribution and a given mean value. 

(1.2) Describe how the departure time of a packet from the system (completion of transmission) is calculated given the arrival time of the packet (the time the packet joined the end of the queue). Make reference to any relevant variables that are used in the code to represent the current state of the system.

(1.3) Give a brief description of the functioning of the event processing loop, making reference to insertion and removal/processing of arrival, departure and statistic events during a simulation run. 

Set up your simulation according to the parameters of Scenario I and with a mean offered load of ρ=0.8 Erlangs. Run the simulation for 1000 simulated seconds. Gather mean delay statistics in batches of 10 sec intervals (100 batch means in total). 

(1.4) Show how you have calculated the mean service rate μ (packet transmission rate) and the mean arrival rate λ for the given offered load. 

(1.5) Plot the set of batch means against time (e.g. using MS Excel, Matlab or Scilab) and calculate the mean value over all batches. 

(1.6) Calculate the 95% confidence interval, stating the answer as a ± percentage. Show your calculation, briefly commenting on the method/formulae you have used. 

(1.7) Compare your mean simulation result to the theoretic result for system delay in the M/M/1 queuing system (see [4], pp. 61). Show your calculation. Comment briefly on the result. 

(1.8) Using the analytic formula, plot a graph for mean system delay for the range of offered load values ρ = {0.4, 0.5, 0.6, 0.7, 0.8, 0.9} (with load values as x-axis).

 

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  • Posted on : June 25th, 2019

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