QoS ROUTING USING GENETIC ALGORITHM (QOSGA)
International Journal of Computer and Electrical Engineering
The current Internet architecture supports best-effort data delivery by default, which has provided satisfactory services for various applications, such as the email and file transfer, to a great extent. On the other hand, the increase in real-time multimedia applications such as Voice over IP, audio and video streaming in the public Internet demand for a Quality of Service (QoS) routing that satisfies multiple constraints such as bandwidth, delay, delay jitter, packet loss, cost, etc. To find
... feasible path satisfying multiple constraints is NP-complete . Hence the recent researches on QoS based routing have triggered the proposition of many heuristic QoS routing algorithms -, , . The time taken by these heuristics to find a feasible path is high . To search all feasible paths in less time, many researchers have used the concept of Genetic Algorithm (GA), which is a new computational strategy inspired by natural processes. The aim of these routing algorithms is to aid the fast selection of a feasible path, which should be adaptive, flexible, and intelligent for efficient network management. The focus of this paper is to develop a GA based routing algorithm that satisfies multiple constraints requirement of the multimedia applications. Hence, in this paper a heuristic called QoS ROUTING USING GENETIC ALGORITHM (QOSGA), which incorporates multiple constraints required by multimedia applications to find a feasible path, has been proposed, designed, and simulated. The processing time taken by the proposed algorithm has been compared with the existing non-GA based heuristic Self Adaptive Multi-Constrained Routing Algorithm (SAMCRA). Also, the number of generations taken by QOSGA to find a feasible path is compared with the number of generation taken by the GA based algorithms1 Genetic Load Balancing Routing Algorithm (GLBR) and Adaptive Routing method based on Genetic Algorithm with two QoS constraints (ARGAQ). The results confirm that QOSGA performs better in terms of time taken to return feasible paths.