Performance Aware Planning Algorithms for Cloud Environments

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Author(s): Jyoti Thaman (Department of Computer Science and Engineering, Maharishi Markandeshwar University, Mullana, Ambala, India)and Kamal Kumar (University of Petroleum and Energy Studies, Dehradun, India)
Copyright: 2018
Volume: 9
Issue: 1
Pages: 15
Source title: International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.2018010101

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

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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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