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Performance Aware Planning Algorithms for Cloud Environments

Performance Aware Planning Algorithms for Cloud Environments

Jyoti Thaman, Kamal Kumar
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 15
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781522545323|DOI: 10.4018/IJDST.2018010101
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MLA

Thaman, Jyoti, and Kamal Kumar. "Performance Aware Planning Algorithms for Cloud Environments." IJDST vol.9, no.1 2018: pp.1-15. http://doi.org/10.4018/IJDST.2018010101

APA

Thaman, J. & Kumar, K. (2018). Performance Aware Planning Algorithms for Cloud Environments. International Journal of Distributed Systems and Technologies (IJDST), 9(1), 1-15. http://doi.org/10.4018/IJDST.2018010101

Chicago

Thaman, Jyoti, and Kamal Kumar. "Performance Aware Planning Algorithms for Cloud Environments," International Journal of Distributed Systems and Technologies (IJDST) 9, no.1: 1-15. http://doi.org/10.4018/IJDST.2018010101

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Abstract

For the last decade, cloud computing has been spreading its application base from the small enterprises to the large, from the domestic user to the professional, from buyers to sellers and from research to implementation. Subscribers submit their jobs or workflows for executions on clouds. Workflow scheduling is a very important aspect in cloud computing and it imitates industrial operations, constraints and dependencies. Several approaches such as Greedy, Heuristic, Meta-heuristic and Hybrid have been tried to reschedule workflows. This article proposes Modified HEFT (MHEFT) and Cluster Based Modified HEFT (C-MHEFT). MHEFT modifies the mapping of ranked tasks to the VMs. C-MHEFT is the cluster based extension of MHEFT. The simulations were performed in WorkflowSim and were compared with existing benchmarks in planning algorithms like HEFT and DHEFT. The proposed schemes will help industries, enterprises to model and sequence the Industrial process which will be faster and efficient.

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