Developing Routing Algorithms with Load Balancing Over Cloud In VANET

Abstract

The expression cloud arrived from the symbol used to embody internet or net. It is gaining lot of popularity as it makes available low cost access to soaring computing resources and tremendously large storage spaces with high level security. Cloud computing refers to many different types of services and applications being delivered over the may produce optimal solution within polynomial time to solve these problems. The idea of task scheduling in cloud environment is to optimize the execution time so that the cost paid by the user is minimal. Task scheduling is the process of distributing workloads across multiple computing resources. Load balancing is an optimization problem and goal of any optimization is to either minimize effort or to maximize benefit. The effort or the benefit can be usually expressed as a function of certain design variables. Hence, optimization is the process of finding the conditions that give the maximum or the minimum value of a function. Load balancing is problems where you try to minimize value of parameters like Make span time, Response Time, etc. and increase the utilization of cloud resources. In this thesis, performance of different stochastic meta heuristic algorithms is compared with the proposed algorithm. Static algorithms are easy to implement but they fail to provide even acceptable solutions. Ant based algorithms are very popular for task scheduling related problems in cloud computing. Particle Swarm Optimization is a swarm based meta heuristic algorithm influenced by the social behaviour of animals such as bird or fish. PSO has fewer primitive mathematical operators than other metaheuristic algorithms which results in lesser convergence time and is applied to continuous value problems. Ant Colony Optimization is also a swarm based meta heuristic algorithm inspired by the behaviour of real ants looking for the shortest path between their colonies and a source of food. newline