Achieving Multi-Objective Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach

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

@InProceedings{Yu:2021:CEC,

• author = "Yongbo Yu and Hui Ma and Gang Chen2",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Achieving Multi-Objective Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "1880--1887",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, Processor scheduling, Heuristic algorithms, Evolutionary computation, Scheduling, Task analysis",
• isbn13 = "978-1-7281-8393-0",
• DOI = "doi:10.1109/CEC45853.2021.9504695",
• abstract = "Traditional human-designed heuristics-based algorithms are commonly employed to address workflow scheduling problems. Such as Heterogeneous Earliest Finish Time (HEFT) and CriticalPath are two best-known list based heuristics. Generally, heuristics-based approaches can generate a single heuristic by making decisions based on the current status of the tasks and available resources and map the unscheduled tasks to the available resources. However, traditional heuristics can easily cause unbalancing load problem. For example, with the objective of minimizing the makespan, traditional heuristics prefer to use computation resources with high computation capacity. Usually, such resources are expensive, which will lead to a high cost as a result. Therefore it is hard for single traditional heuristics to do a trade-off; thus, traditional heuristics are hard to apply for multi-objective workflow scheduling. we develop a novel algorithm for workflow scheduling by considering both minimizing cost and makespan. Experiments show that the MOSGP approach can effectively minimize makespan and cost simultaneously.",
• notes = "Also known as \cite{9504695}",

}

Genetic Programming entries for Yongbo Yu Hui Ma Aaron Chen

Citations