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Multi-Tree Genetic Programming Hyper-Heuristic for Dynamic Flexible Workflow Scheduling in Multi-Clouds
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DOI：10.1109/TSC.2024.3394691.png)
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Abstract
En 中文
Multi-cloud is a promising paradigm due to its advantages such as avoiding vendor lock-in and optimising costs. This article focuses on dynamic flexible workflow scheduling with minimum total monetary cost in multi-clouds, considering multiple categories of services for each cloud with different configurations and billing methods. Existing studies generally ignore the characteristics and states of each individual cloud when making schedules, which may be ineffective regarding cost savings and quality of service. To address this issue, we propose to introduce a cloud selection decision on top of the existing task selection and resource selection decisions to help us select appropriate resource for task in an overall cost-effective cloud. To automatically learn the task, cloud and resource selection rules simultaneously, we propose a new genetic programming with multi-tree representation based on a customised discrete event-driven dynamic workflow scheduling simulator. Simulation results based on two real-world data traces show that the proposed algorithm performs significantly better than the state-of-the-art algorithms in terms of reducing the rental costs and deadline deviation, and improving the success rate. The results also show that the superiority of the proposed algorithm lies in the ability to select an appropriate cloud resource for a task.
Keywords:
Dynamic scheduling
Cloud computing
Task analysis
Costs
Heuristic algorithms
Processor scheduling
Genetic programming
Deadline constraint
dynamic workflow scheduling
genetic programming
multi-clouds
hyper-heuristic
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