In cloud computing, task scheduling plays an important key role. The tasks provided by the user are to be allocated to the resources in cloud and the users have to pay for the usage. Even though there are number of popular schedulers available for task scheduling in Grid and other distributed environments, they are not suitable for cloud. Cloud is different from other distributed environments in resource pool and encounters less failure rate. Task scheduling in cloud has to give attention to the QoS parameters such as deadline and budget. Most conventional heuristic algorithms are proposed in the literature. But the meta-heuristic algorithm like fish swarm approach for the task scheduling in cloud is expected to give way the optimal results. A new meta-heuristic technique inspired from the swarm intelligence of fish, namely Modified Artificial Fish Swarm (MAFS) Optimization for Efficient Task Scheduling in Cloud Environment, has been proposed to solve the task scheduling problem. Then the proposed algorithm is compared with existing algorithms such as Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The experimental result shows that the proposed MAFS greatly reduces the makespan and execution cost.

Task scheduling, resource utilization, cost, makespan

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