Published Paper Details:

In this paper, presentation of different static & 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. Autonomous agent-based load balancing algorithm (A2LB) is a dynamic agent based resource scheduling algorithm that provides scalability and reliability by offering better resource utilization, and minimum response time, but it results in high degree of migration. Particle Swarm Optimization (PSO) 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 (ACO) 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. Ant Colony Optimization algorithm is suited for solving discrete problems and can be used in solving the cloud computing resource management and job scheduling. Algorithm proposed in thesis tries to overcome the limitations of ACO & PSO.