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Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization

Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization

Phani Praveen S., K. Thirupathi Rao
Copyright: © 2018 |Volume: 7 |Issue: 1 |Pages: 17
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522544807|DOI: 10.4018/IJNCR.2018010102
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MLA

Phani Praveen S., and K. Thirupathi Rao. "Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization." IJNCR vol.7, no.1 2018: pp.15-31. http://doi.org/10.4018/IJNCR.2018010102

APA

Phani Praveen S. & Rao, K. T. (2018). Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization. International Journal of Natural Computing Research (IJNCR), 7(1), 15-31. http://doi.org/10.4018/IJNCR.2018010102

Chicago

Phani Praveen S., and K. Thirupathi Rao. "Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group Optimization," International Journal of Natural Computing Research (IJNCR) 7, no.1: 15-31. http://doi.org/10.4018/IJNCR.2018010102

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Abstract

Often cloud providers and cloud clients illustrate several constraints and thus allocation of resources in a heterogeneous cloud is a difficult job. As the traffic flow is quite subjective and Client necessities and applications size vary regularly, the major challenge and concern is to map the external job requests to available virtual machines. To reduce the gap among regularly altering client requirements and existing resources, Client-Awareness Allocation of Resources and Scheduling of jobs in cloud by using social group optimization (SGOCARAJS) is proposed. This algorithm is mainly split into two phases namely allocation of resources using SGO and shortest job first scheduling. The main aim is to map the jobs to virtual machines of cloud group to attain higher client satisfaction and lowest makespan time. Experiments are conducted on datasets and results are compared with present scheduling techniques. This model proved that this algorithm outrun the available algorithms based on concerned metrics.

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