2. Add extra monitors.—LJ Staff

3. Analyse water level and precipitation data.—Keith Nunn

4. Analysis of remote sensing imagery.—Micha Silver

7. As the basis for FOSS conferences.—moose

Copyright @ 200th issue of Linux Journal.

~~1. Actually work instead of waiting for reboots.—Tim Chase~~

2. Add extra monitors.—LJ Staff

3. Analyse water level and precipitation data.—Keith Nunn

4. Analysis of remote sensing imagery.—Micha Silver

~~5. Antagonize Windows users.—John Abbott~~

~~6. Anything I need, since 1994.—Manuel Trujillo~~

7. As the basis for FOSS conferences.—moose

~~8. Audio chat.—LJ Staff~~

~~9. Automate tasks with bash.—Dusty Roberson~~

~~10. Avoid using Microsoft Windows!—Simon Quantrill, Chris Szilagyi~~

2. Add extra monitors.—LJ Staff

3. Analyse water level and precipitation data.—Keith Nunn

4. Analysis of remote sensing imagery.—Micha Silver

7. As the basis for FOSS conferences.—moose

The purpose of this project will be to simulate the performance of a first-in-first-out (FIFO)

queue with fixed sized packets and Markov arrivals to the queue. The simulation will be

performed using only one input source to the queue.

The following parameters will be used throughout the entire project:

c = link speed = 10 Mbps

p = packet size = 4000 bits

Create your own, newly created simulation program for simulation of the queue.

You should write a program that does the low-level management of an event list as described in

the lecture.

Your purpose will be to simulate the performance of the queue for various packet arrival rates.

Within one simulation, you must simulate many packets and find the average response time for

those packets. Then you should perform multiple iterations of the simulation so as to compute

the average response times for packets through the M/D/1 queue across several simulation runs.

Use the following parameters:

λ = arrival rates = 1000 to 2000 in steps of 100

10 simulation runs per arrival rate to compute the average response times

The simulation time should be long enough to simulate 1000 packet arrivals.

To generate the exponential random number needed to create the interarrival times for the

packets, you can use your programming environment’s random number generator. Use the

function that generates a random number X uniformly between 0 and 1. Then use the following

formula to create an exponentially distributed random number Y, where ln(X) is the natural

logarithm function.

A single plot that includes the response time for each simulation at each arrival rate,

the average response time at each arrival rate, and the theoretical response time as

given by the following equation.

#!/usr/bin/env python # Copyright (C) 2010-11-13 by Antonio081014 import random import numpy import matplotlib.pyplot LinkRate = 10000000 #kbps; pkgSize = 4000 #bits; simTimes = 10 #Number of simulations; longda = numpy.arange(1000, 2001, 100) pkgNum = 2000 servTime = 1. * pkgSize / LinkRate M = servTime def getArrivalTime(lnda): r = random.random() return -numpy.log(r) / lnda; def performance(rate): startTime = 0. deptTime = servTime totalTime = servTime for i in range(1, pkgNum): startTime += getArrivalTime(rate) if deptTime - startTime <= 0.: deptTime = startTime + servTime totalTime += servTime else: totalTime += deptTime + servTime - startTime; deptTime += servTime return 1. * totalTime / pkgNum def simulate(): ret = numpy.zeros((simTimes, longda.shape[0]), 'float') for count in range(simTimes): for r in range(longda.shape[0]): rate = longda[r] ret[count][r] = performance(rate); return (ret)*1000. def getTheory(lnda): ld = numpy.zeros(lnda.shape[0], 'float') for i in range(lnda.shape[0]): d = lnda[i] temp = (2.*M - d*(M**2))/(2.*(1.-d*M)); ld[i] = temp return ld*1000. def plotSimulate(record): m = record.shape[0] n = record.shape[1] sum = numpy.sum(record, axis=0); temp = numpy.arange(n)*100 + 1000 matplotlib.pyplot.plot(temp, sum/m, 'g', label='Simulated Average Response Time'); matplotlib.pyplot.plot(temp, getTheory(longda), 'r', label='Theoretical Average Response Time'); for i in range(n): for j in range(m): matplotlib.pyplot.plot(i*100+1000, record[j][i], 'r*'); matplotlib.pyplot.legend(); matplotlib.pyplot.xlabel('Arrival Rate (packages per second)'); matplotlib.pyplot.ylabel('Response Time (ms)'); matplotlib.pyplot.title('Simulated Response Time for an M/D/1 Queue'); if __name__ == '__main__': record = simulate(); # print record matplotlib.pyplot.ion() matplotlib.pyplot.figure() plotSimulate(record); matplotlib.pyplot.savefig('SimulationT.png')

Two plots of actual queue fill with respect to time for one simulation. Show the queue

fill at the time of each packet arrival before the packet is entered into the queue or

serviced. The first plot should be for an arrival rate of 2000 packets per second, and

the second plot should be for an arrival rate of 3000 packets per second.

#!/usr/bin/env python # Copyright (C) 2010-11-13 by Antonio081014 # Single arrival rate specified import random import numpy import matplotlib.pyplot LinkRate = 10000000 #kbps; pkgSize = 4000 #bits; simTimes = 10 #Number of simulations; longda = numpy.array([2000, 3000]) pkgNum = 1000 servTime = 1. * pkgSize / LinkRate M = servTime def getArrivalTime(lnda): r = random.random() return -numpy.log(r) / lnda; def performance(rate): start = numpy.zeros((pkgNum), 'float'); dept = numpy.zeros((pkgNum), 'float'); startTime = 0. deptTime = servTime totalTime = servTime start[0] = startTime dept[0] = deptTime for i in range(1, pkgNum): startTime += getArrivalTime(rate) if deptTime - startTime <= 0.: deptTime = startTime + servTime totalTime += servTime else: totalTime += deptTime + servTime - startTime; deptTime += servTime start[i] = startTime dept[i] = deptTime return start*1000., dept*1000. def simCount(rate): start,dept = performance(rate) count = numpy.zeros((pkgNum)); for i in range(pkgNum): for j in dept[:i]: if j > start[i]: count[i]+=1 return count def plotSimulate(rate): record = simCount(rate); m = record.shape[0] temp = numpy.arange(m) matplotlib.pyplot.plot(temp, record, 'r*', label='The situation of filled queue before next package\'s arrival'); matplotlib.pyplot.legend(); matplotlib.pyplot.xlabel('Package Number'); matplotlib.pyplot.ylabel('Number of package in the queue when its comming'); matplotlib.pyplot.title('The sistuation of filled queue before next package\'s arrival when arrival rate is ' + str(rate)); if __name__ == '__main__': matplotlib.pyplot.ion() matplotlib.pyplot.figure() plotSimulate(longda[0]); matplotlib.pyplot.savefig('Rate01.png') matplotlib.pyplot.figure() plotSimulate(longda[1]); matplotlib.pyplot.savefig('Rate02.png')

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