A Genetic Algorithm (ga) is used to optimise the parameters for a sequence of packets sent over the Internet. Only the parameters
that a client machine can change are used and the fitness is based on the
delay time returned by the Traceroute program. The ga performance is
compared to a fixed packet size with no priority used to assess the status
of the network. The ga generally performed to the same level as the
control settings but in some cases significant improvements were made

Chris J. Hinde (7167710)

Jason Cooper (1171806)

Mark S. Withall (7168622)

Roger Stone (1256976)

English

Loughborough University Institutional Repository

   This item was submitted to Loughborough’s Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.      For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/  Packet Transmission Optimisation using GeneticAlgorithmsMark Withall, Chris Hinde, Roger Stone, and Jason CooperDepartment of Computer Science, Loughborough University, Loughborough, Leics.LE11 3TU, United Kingdom{m.s.withall2,c.j.hinde,r.g.stone,j.l.cooper}@lboro.ac.ukAbstract. A Genetic Algorithm (ga) is used to optimise the parame-ters for a sequence of packets sent over the Internet. Only the parametersthat a client machine can change are used and the fitness is based on thedelay time returned by the Traceroute program. The ga performance iscompared to a fixed packet size with no priority used to assess the statusof the network. The ga generally performed to the same level as thecontrol settings but in some cases significant improvements were made.Keywords. Internet Applications, Genetic Algorithms, Adaptive Con-trol1 IntroductionVarious work has been done on using heuristic and adaptive techniques, such asGenetic Algorithms (ga), to solve networking problems (see for example [4, 6,14]). The main focus of this work has been on areas such as network topologydesign, routing table construction and performance analysis. Most of these areasinvolve having some form of control or access to large areas of the network. Thiswork looks at what performance increase, if any, can be gained from the per-spective of a single client machine on the Internet, without having any externaleffect on the network other than the packets sent.The aims of the experiments were to find good parameters for a sequenceof packet transmissions to optimise the time taken to send 10,000 bytes of data(experiment 1) and minimise the delay of the slowest packet (experiment 2), overthe Internet. The parameters being varied were the packet size, and hence thenumber of packets, and the priority settings of the packets. The optimisation wasperformed using a Genetic Algorithm[9] and was compared to a control setting,to monitor the state of the network, of the maximum packet size and no prioritysettings.2 Genetic AlgorithmsGenetic Algorithms (ga) began in the 1950s, some of the early papers being byFraser [7, 8] and Bremermann [1]. Later on work by Holland[10–12] popularisedgenetic algorithms and Holland’s work is often cited as the origins of GeneticAlgorithms. gas are based on Darwin and Wallace’s theories of Natural Selec-tion[5, 18] and Gregor Mendel’s theory of Genetic Inheritance[15]. A ga takes apopulation of possible solutions to a given problem (individuals), evaluates thesesolutions based on some criteria (fitness) and then genetically recombines thesolutions based on the fitness of the individuals (using genetic operators) in thepopulation to form a new generation of the population. This process is repeateduntil some termination criterion is met. Figure 1 gives the basic flow diagramfor a ga.StartStopCreate Initial PopulationEvaluate Population FitnessCreate New Population From OldTerminate?YesNoFig. 1. Flow diagram for the basic Genetic Algorithm2.1 RepresentationThe way that the individuals are represented (genome) can have a great effecton the performance of the ga. Traditionally, ga individuals are represented asbinary strings, however, any representation that is appropriate for the problemcan be used.2.2 Initial PopulationThe initial population of a ga is important as it specifies the gene values whichthe ga has to work with. This gene pool can then be expanded using mutationas the ga runs. It is usual to start with a randomly generated population.2.3 Fitness EvaluationThe fitness evaluation, for the ga, determines how good an individual from apopulation is at solving a given problem. The fitness is traditionally given assome integer or real number value, where the higher the value the greater thefitness. The fitness value is used to select parents for the reproduction stage ofthe algorithm.2.4 ReproductionParent selection is used to determine which individuals from a population willbe used to create the next generation of individuals. Genetic operators are usedto manipulate the genes of the selected parents to create new individuals for thenext generation. There are two main genetic operators: crossover and mutation.Crossover: This consists of combining the genes from two or more parent in-dividuals to create a new individual. This can take the form of picking arandom point on the genome and using the genes before this point from oneparent and those after from the other. Multiple points can be chosen andused in the same way. At the most extreme, a decision can be made for everygene for which parent to take it from.Mutation: This consists of changing one or more genes in the individual to anew random value.2.5 Termination ConditionsThe two main methods of terminating a ga are to pre-specify a number ofgenerations to run the algorithm for or to run until a member of the populationreaches a specific fitness level.2.6 VariablesBoth the population size and the probability of a mutation can be instantiatedwith different values. The values set can greatly affect the performance of thega.3 Experimental ProcedureThe following subsections describe the particular ga being used for the experi-ments. The first subsection describes the representation being used for the indi-viduals in the population. The second subsection describes the method of fitnessevaluation for the individuals in each experiment. The third subsection describesthe genetic manipulation operators being used and other parameters for the ga.Finally, the hardware and software used for testing is summarised.3.1 Problem RepresentationThere are two parameters being varied in this experiment: the packet size andthe priority. As two parameters would be too few to be usefully varied, the valuesare represented in binary. The packet size is represented as an 11-bit value (0–2047), however, the packet size is restricted to being between 500 and 1460 bytesin length, plus a 40 byte header. If the packet size is less than 500 it is roundedup and if greater than 1460 it is rounded down. The priority is represented as an8-bit value which directly maps to the 8 bits for priority in the packet header,shown in Figure 2. Therefore, the representation of each individual is a 19-bitbinary string which means there are 524288 possible settings being searchedthrough, although some will be functionally the same.---------------------------| DS Codepoint | Unused | DS Field| (6 bits) | (2 bits) |---------------------------Bits 0-2 define class Bits 3-5 define relative priority with class[Format from http://www.itec.uni-klu.ac.at/~hellwagn/RN-QoS/rn2-kap5-DiffServ.pdf]Fig. 2. The DiffServ priority octet3.2 Fitness EvaluationThe fitness evaluation for the ga was conducted by recording the delay time foreach packet of a 10,000 byte message being sent over the Internet. For exper-iment 1, the total time taken (sum of packet delays) to send the 10,000 byteswas used as the fitness value. For experiment 2, the time taken (delay) for theslowest packet was used as the fitness value. The data was sent to an IP addressin Japan, as the path was quite a slow one. The source and destination IP ad-dresses are given in Figure 3. To test the effectiveness of a transmission strategytakes a week or two. The method used here will not tell us the effectiveness ofthe individual except at the time of the test. The reason for using this methodis that it is quicker and that it will show if there are improvements to be madeat times. If it shows that there are times where the ga performs better than thecontrol then it would be worth running a ga using a partial fitness function [2]to evolve transmission strategies.The data was sent using the Traceroute program with the following commandline switches:traceroute -q 1 -n -S 28 -t <priority> <address> <packetsize>The -q switch specifies how many packets to send, the -n switch keeps outputconcise, the -S switch specifies only to print the information for the destinationaddress and not intermediate servers and the -t switch specifies the priority.The Internet158.126.96.6202.33.28.186Fig. 3. Source and destination addresses used for the experimentThe shorter the time, for both experiments, the better. This value is invertedand normalised so that the higher the value the fitter the individual, for easierselection at the reproduction stage.In addition to the ga tests, a control was run with no priority and a packetsize of 1500 bytes (including header). This control was run once for every indi-vidual in the population i.e. ten individual runs and ten control runs.3.3 Genetic Operators and ParametersThe ga uses two types of genetic operator. The first is a crossover operator,where two parents are chosen and some genes from one parent and some fromthe other are used to create the new individual. The crossover is uniform i.e.performed at every point in the genome[16]. The second genetic operator ismutation, where one gene value is randomly changed to a new random value.All new individuals are created by the crossover of two parent individuals withsome probability of mutation in the new individual.The population size used for the experiment was 10 individuals and themutation rate was a probability of 1 gene in 100. The population was initialisedrandomly.3.4 Hardware and SoftwareThe ga was written in Perl version 5.6.1[17] and used Traceroute version 6.0gold. The experiments were run on a Sun Sparc 4 with a Debian Linux operatingsystem. The machine was connected to the Internet over a 10Mb/s switched lanand to the SuperJanet network.4 ResultsThe following sections give the results for the two experiments. The aim ofexperiment 1 was to minimise the total delay to send 10,000 bytes of data overthe Internet and the aim of experiment 2 was to minimise the delay for theslowest packet in the same transmission.4.1 Experiment 1The graph in Figure 4 shows the best control at each generation against the bestevolved individual at each generation.2000250030003500400017/05 18/05 19/05 20/05 21/05 22/05 23/05 24/05 25/05 26/05 27/05 28/05Delay (ms)TimeNetwork Optimisation GAControlGAFig. 4. Delay against time for the best control and best ga solution, at each generation,for experiment 1For most of the experiment, the best ga individual and the best control per-formance are virtually identical. The ga quickly learns that the largest packetsizes perform best. There are two main occasions where the ga performs signif-icantly better than the control. These occur at around generation 3000 (∼00:0021/05) and generation 4440 (∼18:00 21/05). The priority settings at these pointsare generally 199 (class 6, priority 1) and 207 (class 6, priority 3) in the firstinstance and 227 (class 7, priority 0) and 243 (class 7, priority 4) in the sec-ond. The result seems logical as the higher priority classes would be likely toreceive better service. However, it is uncertain why the ga can not maintain thisperformance.4.2 Experiment 2The graph in Figure 5 shows the best control at each generation against the bestevolved individual at each generation.20030040050060070080028/05 29/05 30/05 31/05 01/06 02/06 03/06 04/06 05/06 06/06Delay (ms)TimeNetwork Optimisation GAControlGAFig. 5. Delay against time for the best control and the best ga solution, at eachgeneration, for experiment 2The results for experiment 2 are quite different to those in experiment 1. Thega in most cases manages to keep up with the performance of the control whenthe network is busy but when the network is quiet it can outperform the control.The results show that the ga uses a large packet size when the network is busy.This seems reasonable as sending less packets decreases the chance that one ofthem will be held up in the congestion. When the network was quiet, the ga useda small packet size. This gives enough of a performance improvement to beat thecontrol with a fixed maximum packet size when there is less chance of a packetbeing held up in traffic. The priority settings being used in the three main areasof improved performance were 33 (class 1, priority 0, at ∼20:00 29/05), 120 (class3, priority 6, at ∼19:00 30/05) and 123 (class 3, priority 6, at ∼00:00 02/06).It seems as if the main performance improvement for the ga in this situation iscaused by the decrease in packet size rather than the priority setting.5 Summary and ConclusionsFor experiment 1, the ga performed, in general, at the same level as the controlsettings. However, for two periods the ga showed a significant improvement overthe control by using high priority classes. Unfortunately, this performance couldnot be maintained. For experiment 2, the ga performed well during the periodsof low network congestion but only to the level of the control settings or worseduring high congestion periods.In conclusion, the ga managed to adapt to different conditions on the net-work, and different fitness requirements, quite well but was inconsistent in itsability to outperform the control settings’ performance. Although external fac-tors play the major role in determining transmission delays the experiments haveshown that some improvements can be made from the perspective of an indi-vidual machine. Further work is required to find if any consistent improvementscan be obtained over a simple fixed parameter setting.Alternative approaches to the problem include a Nested Evolution Strategy,which has been shown to be good at many adaptive problems. Genetic Pro-gramming could be used to produce a parameter control program to adjust thepacket settings based on the performance of previous packets sent (and perhapseven other known facts about the network being used). This approach wouldprobably be an improvement of the simple ga, which is just reacting blindlyto the changing conditions of the network without memory of previous networkchanges. The disadvantage to this approach is that a reasonable complete fitnessevaluation would take about a week per individual (or even more) and thereforeit would take a very long time to evolve good solutions.Partial fitness functions [2] is a promising approach which uses an individualsfitness combined with the age of the individual, this allows the fitness testing tocontinue to completion but also uses early results to permit promising individualsto breed. A study using evaluations of over a week shows that the most effectivepolicy depends on the destination and route taken. For example, priority settingson one route which produce good transmission times fail to replicate across arange of routes but do replicate across limited sets of routes. This indicates thatthe optimum strategy depends on the destination and route chosen [3].6 AcknowledgementsThanks to Nortel for their support, both intellectual and financial, during theproject.References1. Bremermann H.J. (1962). Optimization through evolution and recombination. in [19].pp. 93-106.2. Cooper J.L. & Hinde C.J. (2003). Improving the performance of Genetic AlgorithmsUsing Partial Fitness Functions. Submitted to GECCO 2003.3. Cooper J.L. Withall M.S. Hinde C.J. & Stone R.G. (2003). Investigation into theeffects of varying the parameters of packets travelling across the Internet. Lough-borough University Department of Computer Science Internal Report no. 1070.4. Corne D. Smith G. & Oates M. (2000). Telecommunications Optimization: Heuristicand Adaptive Techniques. John Wiley and Sons Ltd.5. Darwin C. (1996). The Origin of Species. Oxford University Press. First published1859.6. Di Caro G. & Dorigo M. (1998). AntNet: Distributed Stigmergetic Control for Com-munications Networks. In Journal of Artificial Intelligence Research, 9:317-365.7. Fraser A.S. (1957a). Simulation of Genetic Systems by Automatic Digital Computers1, Introduction. Australian J. of Biol.Sci., Vol. 10, pp. 484-491.8. Fraser A.S. (1957b). Simulation of Genetic Systems by Automatic Digital Com-puters 2, Effects of Linkage on Rate of Advance under Selection. Australian J. ofBiol.Sci., Vol. 10, pp. 492-499.9. Goldberg D.E. (1989). Genetic Algorithms in Search Optimization and MachineLearning. Addison-Wesley Publishing Co. Inc.10. Holland J.H. (1973). Genetic Algorithms and the Optimal Allocation of Trials. InSIAM Journal on Computing, 2(2):88-105, June.11. Holland J.H. (1975). Adaption in Natural and Artificial Systems. MIT Press.12. Holland J.H. (1992). Adaption in Natural and Artificial Systems. MIT Press, Sec-ond Edition.13. Langdon W.B.,(2002). Proceedings of the Genetic and Evolutionary ComputationConference 2002,Morgan Kaufmann.14. Liang S. Zincir-Heywood A.N. & Heywood M.I. (2002). Intelligent Packets forDynamic Network Routing using Distributed Genetic Algorithm. In [13]. pp. 88-96.15. Mendel G. (1865). Experiments in Plant Hybridization.16. Syswerda G. (1989). Uniform Crossover in Genetic Algorithms. In Schaffer D.,editor, Proceedings of the Third International Conference on Genetic Algorithms,2-9. Morgan Kaufmann.17. Wall L. Christiansen T. & Schwartz R.L. (1996). Programming Perl. O’Reilly &Associates, Inc., Second Edition.18. Wallace A.R. (1858). On the Tendency of Varieties to Depart Indefinitely Fromthe Original Type. In Journal of the Proceedings of the Linnean Society: Zoology3(9):53-62.19. Yovits M.C., Jacobi G.T. & Goldstein G.D. (1962). Self Organizing Systems. Spar-tan Books, Washuington D.C.

Packet transmission optimisation using Genetic Algorithms

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Packet transmission optimisation using Genetic Algorithms

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