Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task Scheduling is one of the important aspects in Cloud Computing. The Primary Objective of the task scheduling is to effectively map tasks on to the corresponding VMs by minimizing makespan and maximizing utilization of resources. Energy consumption is one of the important parameter which needs to...

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Veröffentlicht in:Wireless personal communications Jg. 126; H. 3; S. 2231 - 2247
Hauptverfasser: Mangalampalli, Sudheer, Swain, Sangram Keshari, Mangalampalli, Vamsi Krishna
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2022
Schlagworte:
Communications Engineering
Computer Communication Networks
Engineering
Networks
Signal,Image and Speech Processing
Cloud computing
Energy consumption
Power cost
CS- Cuckoo search
Task scheduling
Whale optimization algorithm
PSO-Particle swarm optimization
ISSN:0929-6212, 1572-834X
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Zusammenfassung:Task Scheduling is one of the important aspects in Cloud Computing. The Primary Objective of the task scheduling is to effectively map tasks on to the corresponding VMs by minimizing makespan and maximizing utilization of resources. Energy consumption is one of the important parameter which needs to be addressed in Cloud Computing. It needs to be minimized as datacenters of Cloud releases huge amount Carbon dioxide which effects the environment. Many of the researchers proposed various scheduling algorithms in Cloud computing to address makespan and resource utilization. This paper proposes a task scheduling algorithm which schedules the tasks on to appropriate VMS based on the calculation of Task and VM priorities and is modeled by using Whale Optimization algorithm while minimizing energy consumption and power cost at datacenters. Initially we have calculated priorities of Task and VMs to effectively maps tasks onto the VMs and thereby evaluating the multi-objective fitness function which addresses energy consumption and power cost at datacenters. Simulation carried out on CloudSim Simulator and analyzed performance of the proposed algorithm with existing PSO and CS algorithms. Simulation results revealed that proposed algorithm minimizes energy consumption and power cost over the existing algorithms.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-09018-6