A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

[...]each metaheuristic algorithm has its own merits and demerits. [...]hybrid approaches have shown to produce better results [6,7] as they combine heuristic rules with metaheuristic algorithms and have attracted much attention in recent years to solve multi-objective workflow scheduling problems i...

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Bibliographic Details
Published in:Applied sciences 2018-04, Vol.8 (4), p.538
Main Authors: Anwar, Nazia, Deng, Huifang
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Computer science
Critical path
Deadlines
Genetic algorithms
Heuristic
International conferences
metaheuristic
multi-objective optimization
Multiple objective analysis
Objectives
Optimization
Optimization algorithms
Production scheduling
scientific workflow scheduling
symbiotic organisms search
Workflow
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Summary:[...]each metaheuristic algorithm has its own merits and demerits. [...]hybrid approaches have shown to produce better results [6,7] as they combine heuristic rules with metaheuristic algorithms and have attracted much attention in recent years to solve multi-objective workflow scheduling problems in the cloud. The two conflicting objectives of the proposed scheme Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO) are to minimize makespan and to reduce cost along with the efficient utilization of the VMs. [...]the proposed multi-objective approach based on a Pareto optimal non-dominated solution considers the users’ as well as providers’ requirements for workflow scheduling in the cloud. [...]they are only locally optimal and infeasible for large and complex workflow scheduling problems in the cloud. A MOP problem can be formulated as: min f(x)=(f1(x),f2(x),…,fd(x)) subject to x∈ω wherein ω represents the decision space. f(x) consist of d objective functions. Since multi-objective optimization usually involve conflicting objectives, so there is no single solution which can optimize all objectives simultaneously.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8040538