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An Intelligent Network Routing Algorithm by a Genetic Algorithm

An Intelligent Network Routing Algorithm by a Genetic Algorithm. Masaharu Munetomo, Yoshiaki Takai, and Yoshiharu Sato Hokkaido University, JAPAN. In this paper, we propose….

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An Intelligent Network Routing Algorithm by a Genetic Algorithm

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  1. An Intelligent Network Routing Algorithm by a Genetic Algorithm Masaharu Munetomo, Yoshiaki Takai, and Yoshiharu Sato Hokkaido University, JAPAN.

  2. In this paper, we propose….. • An adaptiverouting algorithm which employs genetic operators to realize an intelligent routing which directly observes communication latency of the routes. • Path genetic operators for the routing algorithm which generates alternative routes based on the network topology.

  3. Routing Algorithms in the Internet • Each node forwards communication packets based on its Routing Table. • Routing Algorithms generate routing tables based on network topology. • Two major categories of routing algorithms • Interior Gateway Protocols (IGP) • Exterior Gateway Protocols (EGP)

  4. Interior Gateway Protocol (IGP) • Routing protocols inside an autonomous system (AS) such as a Local Area Network • We have two major protocols for the IGPs commonly used in the Internet: • Routing Information Protocols (RIP) • Shortest Path First Protocols (SPF) • or Open SPF (OSPF)

  5. Exterior Gateway Protocol (EGP) • Routing protocols outside an AS which exchanges routing information among AS’s. • Recently, BGP (Border Gateway Protocols) become popular in the Internet. • The BGP4 employs a source routing approach which determines all the nodes along a route in the source node instead deciding only its next hop.

  6. Routing Information Protocol (RIP) • A distributed algorithm • Each node broadcasts its routing table. • Each node recalculates distances in the routing table on receiving a routing table from its neighbors. Broadcast

  7. Shortest Path First protocol (SPF) • Each node broadcasts its link status. • Each node stores network topology generated from the received link status information and calculates shortest paths by using the Dijkstra’s Shortest Path First Algorithm. • The algorithm can reduce communicationoverhead by broadcasting only link status not all the routing tables.

  8. Problems of the RIP and the SPF • Not scalable: they increase their communication overhead in larger networks. • Not efficient when they need to collect load status of links repeatedly to consider delay along a route to be minimized. Communication Overhead (n : # of nodes in the network)

  9. Genetic-Based Routing (GBR) • Employing source routing and only maintain a set of alternative routes frequently used in communication. • Alternative routes are generated by PathGenetic Operators we propose. • Observing communication latency for the limited number of routes to greatlyreducecommunication overhead for the routing.

  10. Overview of the GBR

  11. Path Genetic Algorithm (pGA) • Encoding paths(routes) by listing up node ID’s, for example, (0 12 5 8 2 9). • We have two path genetic operators: - Path Mutation - Path Crossover • Selection is performed by deleting routes not frequently used in the routing table.

  12. Path Mutation 1. We select a node (nm) from the original route. 2. Another node (n’m) is selected from neighbor of nm. 3. Connecting source to n’m and n’m to destination.

  13. Path Crossover --- Exchanges sub-routes among a pair of routes.

  14. Fitness evaluation and Selection • Each node periodically sends delay query packets to observe communication latency along a route. • Fitness value is calculated from the delay di : delay of route i • Selection is invoked when routing table is overflowed.

  15. Execution flow of the GBR 1. When we need to send a packet, we select a route randomly according to fitness value of routes (roulette wheel selection). 2. After sending a specified number of routes, we send delay query packet to evaluate fitness. 3. After a specified number of delay query, we apply path genetic operators to generate alternative routes in the routing table. 4. If the number of routes exceeds a limit, we perform a selection bydeleting routes with maximum delay.

  16. Simulation Experiments • Using a network simulator written in C++. • Sample network is taken from Japanese geographical info. • Simulation time is 3000s. • Genetic operators are invoked at every 30 evaluation of delay.

  17. Mean arrival time of packets • The GBR achieves much smaller mean arrival time of communication packets a than those of RIP, SPF and an adaptive SPF. • An adaptive SPF which directly observes communication latency of links is not efficient in lightly-loaded networks.

  18. Load status of Links GBR SPF RIP • Thickness of a link stands for its mean queue length. • GBR achieves much less overhead of links, especially • on the link 11 <=> 13 <=> 19.

  19. Conclusions • Path Genetic Algorithm (pGA) we propose creates alternative routes in routing tables. • A genetic based routing (GBR) algorithm can effectively forward communication packets, which leads to smaller arrival time. • Load balancing among links is realized by the GBR algorithm.

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