Hybrid Genetic Swarm Scheduling for Cloud Computing

Hybrid Genetic Swarm Scheduling for Cloud Computing

Dr. M.Sridhar

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Acharya Nagarjuna University

Hybrid Genetic Swarm Scheduling for Cloud Computing

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Abstract

Cloud computing ensures access to shared resources and common infrastructure, offering services on demand over a network for operations to meet changing business needs. Scheduling is a prominent activity that is executed in a cloud computing environment. To increase cloud computing work load efficiency, tasks scheduling is performed to get maximum profit. In cloud, high communication cost prevents task schedulers from being applied in large scale distributed environments. Cloud environment system scheduling is NP-complete. To solve the NP complete and NP hard problems heuristic approaches are used. This study proposes a hybrid optimization based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for scheduling in cloud environments.

References

24 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. M.Sridhar. 2015. \u201cHybrid Genetic Swarm Scheduling for Cloud Computing\u201d. Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 15 (GJCST Volume 15 Issue B3): .

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GJCST Volume 15 Issue B3
Pg. 9- 15

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-B Classification: C.1.3, D.4.1

Submission ReceivedDecember 16, 2014
Peer Review Double Blind
Handling Editor
Accepted December 31, 2014
Published January 15, 2015

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v1.2

Issue date

July 17, 2015

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Article Matrices

Total Score: 106

Country: India

Subject: Global Journal of Computer Science and Technology - B: Cloud & Distributed

Authors: Dr. M.Sridhar (PhD/Dr. count: 1)

View Count (all-time): 314

Total Views (Real + Logic): 8250

Total Downloads (simulated): 2240

Publish Date: 2015 07, Fri

Monthly Totals (Real + Logic):

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Total Views: 8250

Total Downloads: 2240

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