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Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Sandeep Kumar Bothra, Sunita Singhal, Hemlata Goyal
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 34
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799859772|DOI: 10.4018/IJITWE.2021100101
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MLA

Bothra, Sandeep Kumar, et al. "Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling." IJITWE vol.16, no.4 2021: pp.1-34. http://doi.org/10.4018/IJITWE.2021100101

APA

Bothra, S. K., Singhal, S., & Goyal, H. (2021). Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling. International Journal of Information Technology and Web Engineering (IJITWE), 16(4), 1-34. http://doi.org/10.4018/IJITWE.2021100101

Chicago

Bothra, Sandeep Kumar, Sunita Singhal, and Hemlata Goyal. "Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling," International Journal of Information Technology and Web Engineering (IJITWE) 16, no.4: 1-34. http://doi.org/10.4018/IJITWE.2021100101

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Abstract

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

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