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Dynamic multi-objective workflow scheduling for combined resources in cloud
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DOI：10.1016/j.simpat.2023.102835.png)
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摘要
En 中文
Cloud resource providers offer idle resources to users as spot instances. The price of the instances changes with market supply and demand, and the dynamic price can have a significant impact on workflow scheduling. In this work, we use a combination of spot and on-demand instances as the foundation cloud resource and characterize the dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP), where the dynamics originate from the dynamic price of spot instances. The scheduling solution is found by considering three objectives: maximizing the reliability of the instances while minimizing the makespan and cost. In addition, we provide an enhanced MOEAD algorithm called MOEA/D-URDI that combines diversity introduction and uniform random sampling, where the uniform random sampling paradigm is used to generate the initial weight vector. The dynamic multi-objective optimization evolutionary algorithm DMOEA/D-URDI is then created by combining the method with a dynamic optimization framework. Our technique beats existing algorithms, according to experimental data based on dynamic benchmark sets and three well-known scientific procedures in terms of metrics on dynamic benchmark sets and better ensures reliability in scheduling scientific workflows while reducing makespan and cost.
Keyword:
Workflow scheduling
Dynamic price
Dynamic multi-objective evolutionary
algorithms
Spot instances
Initial weight vector construction
期刊
IF:
4.6
论文数:
2.6K
被引数:
4.8K
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A Cost-Effective Framework for Running Industrial Big Data Analysis Applications in Public Clouds
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