AN EVOLUTIONARY APPROACH TO DISTANCE VECTOR ROUTING

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1 International Journal of Latest Research in Science and Technology Volume 3, Issue 3: Page No , May-June ISSN (Online): AN EVOLUTIONARY APPROACH TO DISTANCE VECTOR ROUTING Sunita Beniwal 1, 2,Dharminder Kumar 2 1 Department of Information Technology, Maharishi Markandeshwar University Mullana, Ambala, Haryana , India 2 Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology Hisar, Haryana , India Abstract- Distance vector routing algorithms operate by having each router maintain a table giving the best known distance to each destination and which line to use to get there. Genetic algorithms are search algorithms based on mechanics of natural selection and natural genetics. To overcome some of the problems of traditional adaptive routing techniques genetic algorithms can be used. GA s have the capacity of solving complicated solutions in practical systems. Hardware implementation of GA is very fast and it is not sensitive to network size. GA s are very flexible as quality of solution can be adjusted as a function of population size. GA hardware scale well to network that may not even fit in memory. Due to the above reasons GA offers very promising solutions to many problems. In the proposed work parameters used for calculating the routes are hop count and queue length. For better performance of any network, path which is selected for transmission should be such that path with lowest hop count and shortest queue length is selected. Various parameters i.e. throughput, packet delivery ratio, delay, hop count etc. are analyzed to see the performance of the network with and without using GA in distance vector routing. Keywords: Crossover, Encoding, Fitness Function, Hop Count, Queue Length, Throughput. 1. INTRODUCTION Routing is a fundamental engineering task on the internet. It consists of finding a path from source to destination host. The routing algorithm is that part of network layer software responsible for deciding which output line an incoming packet should be transmitted on. Routing algorithms can be grouped into two major categories: Adaptive and Nonadaptive. Nonadaptive algorithms do not base their routing decisions on measurements or estimates of current traffic and topology. Instead the choice of the route to use to get from source to destination is computed in advance offline and downloaded when network is booted. This procedure is also called static routing. Adaptive algorithms in contrast change their routing decisions to reflect changes in the topology as well as traffic. Adaptive algorithms differ in where they get their information from, when they change the routes, what metric is used for optimization. Modern computer networks generally use dynamic routing algorithms rather than the static ones. Two dynamic algorithms in particular: Distance vector routing and Link state routing, are the most popular. The present work emphasize on distance vector routing technique. In distance vector routing, the router measures the distance directly with the help of special ECHO packets that the receiver just timestamps and sends it back. All the routers should carry out this process simultaneously. Thus regardless of the router being used or not, all of them connected in the network have to transmit the ECHO packets. Thus apart from the normal data packets, the ECHO packets must also be sent by the router in order refresh its routing table [1]. 2. GENETIC ALGORITHM The origin of Genetic Algorithm is attributed to John H. Holland [2]. Genetic algorithms are search algorithms based on mechanics of natural selection and natural genetics. Solutions are represented in the form of individuals (chromosome) which represent knowledge. A chromosome is represented by a vector storing genes. Population of GA is composed of group of individuals. Fitness is computed for all the individuals and the individuals with higher fitness survive and are selected for next generation. Crossover and mutation are genetic operators used for exploiting possible search spaces and for maintaining diversity in the population respectively [3]. 3. RELATED WORK Genetic algorithms have been applied widely to network problems in various domains such as network design problems, shortest path problems etc. Gen et al. [4] considered hybrid GAs (called spanning tree-based GAs) for difficult-to-solve network design problems inherent in industrial engineering and computer communication networks, such as degree-constrained minimum spanning tree problems, capacitated minimum spanning tree problems, fixed charge transportation problems, network topological design problems and so on. Davies and Lingras [5] used ISSN:

2 genetic algorithms for finding the shortest path in a dynamic network, which adapts to the changing network information by rerouting during the course of its execution. The technique proposed by Masillamani et al. [6] for optimization of distance vector routing technique minimizes the congestion by reducing the number of ECHO packets transmitted and uses genetic algorithm to optimize the path selection. In this technique only the routing tables through which the packet is forwarded are updated instead of all as in normal distance vector routing algorithm. The technique proposed optimizes the conventional distance vector algorithm. Selamat and Selamat [7] proposed a method to minimize network routing time taken by the mobile agents to collect information from different sites using genetic algorithm (GA). The proposed mobile agent for routing algorithm tries to minimize the query retrieval cost while maintaining a reasonable path delay. Zhu et al. [8] presented an algorithm for the Point to Multipoint Routing Problem that uses a genetic algorithm and a heuristic Steiner tree algorithm that allows the scheduler to find an optimal or near-optimal path through the network for each request. Genetic algorithm employed use permutation of number of requests for representing a chromosome. By using Steiner tree based routing instead of treating the point to multipoint requests as several point to point requests, there will generally be fewer simultaneous copies of a signal on a link, resulting in less network traffic and a potential for more requests to be sent. Ahn and Ramakrishna [9] presented a genetic algorithm based approach to the shortest path routing problem. A population-sizing equation that facilitates a solution with desired quality is developed based on the gambler s ruin model. The proposed algorithm exhibits a much better quality of solution and a much higher rate of convergence than other algorithms. 4. PROPOSED GENETIC ALGORITHM Distance vector routing technique makes use of the routing tables for calculating routes for incoming packets. Getting route information from routing tables is a tedious and time consuming procedure if network changes very rapidly. The objective of this work is to design a distance vector routing algorithm with elements of genetic algorithm in it so that route calculation become less time consuming and better routes are known faster than the traditional distance vector routing technique. The present work has been carried out using GloMoSim network simulator. For implementation of Genetic algorithm, encoding which is to be used i.e. how chromosomes are represented and used to store data for routing packets is selected. Then value of crossover operator and technique used for crossover is selected while maintaining the feasibility of paths. By feasible paths what is meant is that paths which are generated actually exist and intermediate routers which are there in the path one after another are connected to each other. 4.1 Encoding To solve a problem it is important to select proper encoding. Encoding represents transformation of solved problem to N-dimensional space of real (integer) numbers (mostly). Encoding used is list of nodes for reaching from source to destination. The size of chromosomes is not fixed. It is variable because number of nodes in a route is not fixed and can vary from two to total number of nodes in the network. The maximum size is obviously equal to total number of nodes. As different paths come as a reply to the source, those paths are stored as a sequence of nodes in the chromosome. So the structure of chromosomes is list of nodes in a sequence in which they are used to reach the destination. 4.2 Population Chromosomes store routes. So for initialization of population routes are used. Population is initialized using simple DSR in the beginning. The size of population is taken to be equal to maximum number of nodes in the network. Population of chromosomes is initialized by sending normal route request packets to the destination. After the number of route replies exceeds two GA is applied on them. 4.3 Crossover Crossover is basic chromosome operation used for creation of new individuals. New solutions produced are supposed to be in search space somewhere between parent solutions. Crossover used in this work is one point crossover i.e. crossover is done only at a single point. After the parents are selected randomly for crossover, it is checked whether they have some node in common. If they have some node in common then only crossover is applied otherwise crossover is not done on those chromosomes. After selection of common node the parts after that node are swapped and two new offspring are created and stored in the population. 4.4 Fitness Function Fitness function or objective function is that function which needs to be optimized that is either minimized or maximized according to the problem. In this study fitness function used is inversely proportional to sum of hop count and queue length. Fitness function=1/(hop count + queue length) As can be seen, if the number of nodes between source and destination is large, hop count is also large and so the value of fitness function is small. Similarly longer queue length means lesser value of fitness function. 4.5 Reproduction For selection of chromosomes for next generation roulette wheel selection is used. The chromosomes with high value of fitness function have more probability of selection than the chromosomes with lesser value of fitness. So those chromosomes with lower hop count and queue length have more probability of being selected. 4.6 Genetic Distance Vector Routing When a node has data to be send to another node it checks its route cache table to check for the presence of any path to that node. If path is present it is used for sending packet otherwise a route request is sent to all neighbors. If neighbors have a path to that node they send that information ISSN:

3 to the source node else they forward the packet. This process is repeated until either a path is found or the destination is reached. If the packet reaches the destination it see that the route request is for it only so it collect the path information from the packet and sends back a reply to source telling the route followed by the request path as route to it. Destination may receive multiple route request packets by multiple links connected to it. It sends back all replies to the source. After source receives the replies sent by the destination, GA comes into picture. For the time number of replies is less than two, simple reply handling procedure is carried out. After the number of replies exceeds one GA is applied, selection of chromosomes for next generation is done using the recombination technique described above. Crossover is then applied to selected chromosomes as per the crossover probability. Fitness is then calculated based on the hop count values and queue length. For the next generation, which are more fit that is whose fitness is more are selected more number of times for next generation. This process is repeated for many times unless the solution converges to a best solution. After the convergence the route whichever is best is selected and copied to the route cache table and then that route is used as a path from source to destination. 5.2 Throughput with Varying Number of Nodes As shown in fig 2 the addition of GA to DSR improves the performance of DSR by increasing its throughput. As the number of nodes increase the DSR with GA has more throughput than the DSR without GA. For lesser number of nodes the performance of both algorithms that is with and without using GA is nearly same, the difference comes when number of nodes is more than 50.After 50 number of nodes the performance of DSR with GA keeps on getting better. So it can be concluded from this graph that GA is helpful in case of more number of nodes. 5. RESULTS AND DISCUSSION GA is applied to DSR. The effect of applying GA on DSR is analyzed. In this section we will analyze the performance of the network by changing the various parameters like number of nodes, queue length etc. Then we will analyze the effect of GA on performance of DSR. 5.1 Hop Count v/s throughput Fig 1 shows that as number of hops between sender and receiver increases average throughput decreases. For less number of nodes between sender and receiver the number of packets transmitted per unit of time is more than if number of hops is less that is both are inversely proportional to each other. Fig 2 Average throughput vs No. of nodes 5.3 Hop count with Varying Number of nodes In any network as the number of nodes increase the average hop count of routes selected will increase. As shown in fig 3 as the number of nodes increase the difference between average hop count for with GA and without GA increase. For less number of nodes the average hop count is nearly same in both cases, but as the number of nodes increase the average hop count of DSR with GA decrease that is in this case the route with less hop count are generated and selected for transmission of packets. Fig 3 Average hop count v/s No. of nodes Fig 1 Hop count v/s throughput ISSN:

4 5.4 Average Queue Length and Average Throughput Vs Number of Nodes As can be seen from fig 4 throughput is more for less number of nodes because number of nodes are less which make the path length less and thus increases throughput. For queue length also for lesser number of nodes queue length is less because less number of nodes will transmit fastly on shorter paths. It is also seen from the graph that as queue length increases throughput decreases. It is because for longer queues most of the packets to be transmitted ar queued up waiting to be transmitted and thus more time needed for processing packets on intermediate routers and hence the decreased throughput. Fig 4 Average throughput and average queue length v/s No. of nodes 5.5 Average Throughput vs Average Queue Length Fig 5 Shows The Comparison Of Throughputs between DSR with GA and DSR without GA. As the average queue length is less the throughput is high whereas for more number of packets in queues average throughput is less. In DSR without GA the throughput is less than in case of DSR with GA. It means that GA decreases the probability of selection of a route with greater average queue length whereas in case DSR without GA the route with more average queue length more number of times for transmission before selecting the best possible path. Fig 5 Average queue length v/s average throughput 6. CONCLUSION Genetic Algorithms are nowadays used to solve problems with large search space and which are difficult to solve. GA s have many advantages because of which they are preferred. First of all, GA s have the capacity of solving complicated solutions in practical systems. Hardware implementations of GA are very fast and it is not sensitive to network size. GA s are very flexible as quality of solution can be adjusted as a function of population size. GA hardware scale well to network that may not even fit in memory. So due to the above reasons GA offers very promising solutions to many problems. In this paper an explanation of GA s, Distance Vector Routing technique and their advantages and disadvantages has been presented. Then various encoding, crossover and reproduction operators to be used in the proposed system is discussed. In the present work fitness function used is dependent on two parameters namely hop count and queue length. Only these two parameters affect the routes computed by the GA designed. For future work different variables and their combination can be taken into account to compute the fitness of the routes. Some of the parameters which can be considered are: ISSN: Capacity of the links can be taken as a parameter. Different constraints can be applied like whether to send all packets via same route or the priority of some routes can be set to high depending on their reliability in data delivery. Different combinations of parameters can be used to construct a multi parameter GA which can improve the performance of DSR even more. Advanced crossover scheme should be implemented wherein more than one vector of the two chromosomes can be crossover. REFERENCES Some method to implement mutation may also be devised to maintain diversity in the population. 1. Tanenbaum, A.S., Computer Networks. 4th Edn., Prentice-Hall Inc. 2. Holland, J.H., Adaptation in Natural and Artificial Systems. 1st Edn., University of Michigan Press, Ann Arbor. 3. De Jong, K., Learning with genetic algorithms: An overview, Machine learning, vol. 3, Gen, M., A. Kumar and J.R. Kim, Recent network design techniques using evolutionary algorithms. Int. J. Prod. Econ., 98: Davies C. and P. Lingras, Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks. Eur. J. Oper. Res., 144: Masillamani, M.R., A.V. Suriyakumar, R. Ponnurangam and G.V. Uma, Genetic algorithm for distance vector routing technique. Int. J. Artif. Intell. Mach. Learn., 6: Selamat, A. and M.H. Selamat, Routing algorithm of mobile agents for query retrieval using genetic algorithm. Malaysian J. Comput. Sci., 17: Zhu, L., R.L. Wainwright and D.A. Schoenefeld, A genetic algorithm for the point to multipoint routing problem with varying number of requests. Proceedings of

5 the IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, May 4-9, 1998, Anchorage, AK., USA., pp: Ahn, C.W. and R.S. Ramakrishna, A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans. Evol. Comput., 6 (6): ISSN:

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