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• W4294811634 abstract "With the popularity of cloud computing, many organizations process their workflow tasks in cloud resources based on the Pay-As-Per-Use model. Dynamic Workflow Scheduling (DWS) aims to allocate dynamically arriving workflow tasks to cloud resources with optimal makespan, cost, load-balancing, etc. To timely allocate arriving tasks, heuristics have been used to solve the DWS problem in cloud environment. However, most of them are manually designed, considering a single objective, and use simple features to allocate resources to workflow tasks. In practice, multiple objectives should be considered to provide trade-off heuristics for users to choose from. In this paper, we propose a genetic programming hyper-heuristic (GPHH) approach to automatically generate multiple heuristics for multi-objective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach." @default.
• W4294811634 created "2022-09-06" @default.
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• W4294811634 date "2022-07-18" @default.
• W4294811634 modified "2023-10-12" @default.
• W4294811634 title "A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment" @default.
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• W4294811634 doi "https://doi.org/10.1109/cec55065.2022.9870403" @default.
• W4294811634 hasPublicationYear "2022" @default.
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