A Multi-Objective Memetic Algorithm for Workflow Scheduling in Clouds

Simultaneously optimizing monetary cost and makespan of workflow execution is substantial to enhance the competitiveness of cloud services, but it still imposes challenges. Heuristics-based algorithms are often problem-dependent and well-suitable for special cases, but the complexity of workflow str...

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Vydané v:IEEE transactions on emerging topics in computational intelligence s. 1 - 12
Hlavní autori: Yao, Feng, Chen, Huangke, Liu, Xiaolu, Gong, Maoguo, Xing, Lining, Zhao, Wei, Zheng, Long
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2024
Predmet:
Cloud computing
Computational modeling
Costs
Heuristic algorithms
Indexes
local search
Memetic computation
Memetics
Metaheuristics
multi-objective
Processor scheduling
Scheduling
Search problems
workflow scheduling
ISSN:2471-285X, 2471-285X
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Abstract Simultaneously optimizing monetary cost and makespan of workflow execution is substantial to enhance the competitiveness of cloud services, but it still imposes challenges. Heuristics-based algorithms are often problem-dependent and well-suitable for special cases, but the complexity of workflow structures seriously challenges their generalization. Metaheuristics-based algorithms pose good generalization, but the elasticity and heterogeneity of cloud resources seriously impact their search efficiency. Inspired by previous works, we tailor a memetic workflow scheduling algorithm (KDMA for short) that embeds a heuristic local search operator into the multi-objective metaheuristic algorithm to combine their strengths and complement each other's shortcomings. Specifically, the proposed local search operator searches for a set of solutions with good convergence and diversity by accumulating tasks one by one. This operator is good at gathering workflow tasks into a limited range of candidate resources, thereby guiding the metaheuristic algorithm to focus on exploring potential solution regions. Moreover, KDMA includes an adaptive strategy to determine the number of solutions reproduced by the problem-special local search operator based on its past overall contribution. We compare KDMA to five representative algorithms over 25 real-world workflow instances to corroborate its superior all-around performance by performing the best on 21 workflow instances. Meanwhile, we conduct an ablation analysis to verify the performance contributions of two proposed mechanisms.
AbstractList Simultaneously optimizing monetary cost and makespan of workflow execution is substantial to enhance the competitiveness of cloud services, but it still imposes challenges. Heuristics-based algorithms are often problem-dependent and well-suitable for special cases, but the complexity of workflow structures seriously challenges their generalization. Metaheuristics-based algorithms pose good generalization, but the elasticity and heterogeneity of cloud resources seriously impact their search efficiency. Inspired by previous works, we tailor a memetic workflow scheduling algorithm (KDMA for short) that embeds a heuristic local search operator into the multi-objective metaheuristic algorithm to combine their strengths and complement each other's shortcomings. Specifically, the proposed local search operator searches for a set of solutions with good convergence and diversity by accumulating tasks one by one. This operator is good at gathering workflow tasks into a limited range of candidate resources, thereby guiding the metaheuristic algorithm to focus on exploring potential solution regions. Moreover, KDMA includes an adaptive strategy to determine the number of solutions reproduced by the problem-special local search operator based on its past overall contribution. We compare KDMA to five representative algorithms over 25 real-world workflow instances to corroborate its superior all-around performance by performing the best on 21 workflow instances. Meanwhile, we conduct an ablation analysis to verify the performance contributions of two proposed mechanisms.
Author Zhao, Wei
Liu, Xiaolu
Xing, Lining
Yao, Feng
Gong, Maoguo
Chen, Huangke
Zheng, Long
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Snippet Simultaneously optimizing monetary cost and makespan of workflow execution is substantial to enhance the competitiveness of cloud services, but it still...
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SubjectTerms Cloud computing
Computational modeling
Costs
Heuristic algorithms
Indexes
local search
Memetic computation
Memetics
Metaheuristics
multi-objective
Processor scheduling
Scheduling
Search problems
workflow scheduling
Title A Multi-Objective Memetic Algorithm for Workflow Scheduling in Clouds
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