Research Area:  Cloud Computing
The cloud computing provides on demand access to shared resources over internet in a cloud platform powerfully adaptable and metered way. Cloud computing empowers the user get to wherever to a shared pool of configurable resources and gives different administrations to the resource assignment like scientific operations, services computing through virtualization. To give guaranteed productive execution to clients, tasks ought to be proficiently mapped to accessible resources. In this manner, Task Scheduling is noteworthy issue in the cloud infrastructure administrations. The essential target of task execution planning includes reserving the infrastructure assets and limiting the goal of the execution plan. In this research work, we proposed metaheuristic optimization technique with load balancing to enhance the cloud infrastructure service providers performance there by depleting the scheduling issues. The proposed technique is pertinent for static and dynamic task condition, where static methods VM parameters are fixed, dynamic means parameters are chosen runtime. The proposed algorithm consists of two phases MHOS-S and MHO-D for dealing with static and dynamic properties of the task submitted. The result analysis by comparing with few traditional metaheuristic algorithms proves that the proposed technique performs better in complex environments.
Author(s) Name:  S. Peer Mohamed Ziyath & S. Senthilkumar
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Publisher name:  Springer
Volume Information:  volume 12, pages 6629–6638 (2021)
Paper Link:   https://link.springer.com/article/10.1007/s12652-020-02282-7