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Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

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Title Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment
 
Creator Pradhan, Roshni
Satapathy, Suresh Chandra
 
Subject Cloud computing
Datacenters
Genetic algorithm
Makespan
NP-complete
 
Description 776-784
Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive
energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service
providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which
identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an
alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and
energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the
context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks
mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall
execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique
also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms
like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.
 
Date 2022-07-06T06:45:19Z
2022-07-06T06:45:19Z
2022-07
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/60053
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(07) [July 2022]