Performance evaluation of task scheduling using hybrid meta-heuristic in the heterogeneous cloud environment

Cloud computing is a platform in which it provides services, information and software over the Internet. The essential role of cloud computing is enabling sharing of resources on-demand over the network (e.g. servers, applications, storage, services and database) to the end-users that are distributed geographically. Task scheduling is a significant function in the cloud computing that plays a vital role to raise the rate of efficiency and the performance of the system. Task scheduling is considered as an NP-complete problem. However, the heterogeneity of resources in the cloud environment put the scheduling in a critical issue. Furthermore, heuristic algorithms do not have the required level of efficiency to optimize the scheduling and the performance in this environment. Thus, this study focuses on optimizing the hybrid meta-heuristic (genetic algorithm along with DE algorithm that minimizes the completion time and enhances the performance of the task scheduling. The results will be compared with a three heuristic algorithms. The performance evaluation in this work is a statically analysis that used in an experimental comparison. The expected result of this study is optimizing the overall of completion time and enhancing resource efficiency.

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