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A Fully Hybrid Algorithm for Deadline Constrained Workflow Scheduling in Clouds
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AI
DOI：10.1109/TCC.2023.3269144.png)
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Abstract
En 中文
With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This article formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.
Keywords:
Task analysis
Scheduling
Metaheuristics
Heuristic algorithms
Costs
Cloud computing
Genetic algorithms
Workflow scheduling
heuristic
meta-heuristic
hybrid algorithm
deadline constrained
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