AP: Hybrid Task Scheduling Algorithm for Cloud | Zendy

Bhupesh Kumar Dewangan | Zendy; Anurag Jain | Zendy; Tanupriya Choudhury | Zendy

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AP: Hybrid Task Scheduling Algorithm for Cloud

Author(s) -

Bhupesh Kumar Dewangan,

Anurag Jain,

Tanupriya Choudhury

Publication year - 2020

Publication title -

revue d intelligence artificielle

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.146

H-Index - 14

eISSN - 1958-5748

pISSN - 0992-499X

DOI - 10.18280/ria.340413

Subject(s) - computer science , cloud computing , virtual machine , particle swarm optimization , ant colony optimization algorithms , scheduling (production processes) , load balancing (electrical power) , schedule , genetic algorithm , distributed computing , algorithm , task (project management) , mathematical optimization , real time computing , engineering , machine learning , operating system , mathematics , geometry , systems engineering , grid

Resource optimization is cost effective process in cloud. The efficiency of load balancing completely depends on how the infrastructure is utilizing. As per the current study, the resource optimization techniques are very costly and taking more convergence time to execute the task and load distribution among different virtual machines (VM). The objective of this paper is to develop a hybrid optimization algorithm to find the best virtual machine based on their fitness values and schedule different task to the fittest VM so that each task should get complete on time, and system can utilize the VM as well. The proposed algorithm is hybrid version of genetic (GA), ant-colony (Aco), and particle-swarm (Pso) algorithms, which is implemented and tested in amazon web service and compared with existing algorithms based on VM utilization, completion time, and cost. The proposed hybrid system genetic-aco-pso based algorithm (GAP) perform utmost while comparing with the existing systems.

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