A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yu:2022:CEC,

• author = "Yongbo Yu and Tao Shi and Hui Ma and Gang Chen2",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• title = "A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• isbn13 = "978-1-6654-6708-7",
• 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 multiobjective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach.",
• keywords = "genetic algorithms, genetic programming, Cloud computing, Costs, Processor scheduling, Computational modeling, Organizations, Evolutionary computation, Cloud computing, dynamic workflow scheduling, multi-objective, GPHH",
• DOI = "doi:10.1109/CEC55065.2022.9870403",
• notes = "Also known as \cite{9870403}",

}

Genetic Programming entries for Yongbo Yu Tao Shi Hui Ma Aaron Chen

Citations