DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which...

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Vydané v:Cluster computing Ročník 24; číslo 2; s. 667 - 681
Hlavní autori: Iranmanesh, Amir, Naji, Hamid Reza
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.06.2021
Springer Nature B.V
Predmet:
Big Data
Cloud computing
Computer Communication Networks
Computer Science
Critical path
Efficiency
Energy consumption
Genetic algorithms
Heuristic
Load balancing
Operating Systems
Operators
Optimization techniques
Processor Architectures
Scheduling
Software services
Task scheduling
Workflow
Workloads
Cloud computing
Genetic algorithm
Workflow scheduling
Heuristic
ISSN:1386-7857, 1573-7543
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Shrnutí:Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-020-03145-8