Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Yang:2021:CEC,

• author = "Yifan Yang and Gang Chen2 and Hui Ma and Mengjie Zhang and Victoria Huang",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Budget and {SLA} Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "2141--2148",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, Cloud computing, Schedules, Data centers, Processor scheduling, Heuristic algorithms, Computational modeling, dynamic workflow scheduling, cloud computing",
• isbn13 = "978-1-7281-8393-0",
• DOI = "doi:10.1109/CEC45853.2021.9504709",
• abstract = "Workflow with different patterns and sizes arrive at a cloud data center dynamically to be processed at virtual machines in the data center, with the aim to minimize overall cost and makespan while satisfying Service Level Agreement (SLA) requirement. To efficiently schedule workflows, manually designed heuristics are proposed in the literature. However, it is time consuming to manually design heuristics. The designed heuristics may not work effectively for heterogeneous workflow since only simple problem related factors are considered in the heuristics. Further, most of the existing approaches ignore the deadline constraints set in SLAs. Genetic Programming Hyper Heuristic (GPHH) can be used to automatically design heuristics for scheduling problems. In this paper, we propose a GPHH approach to automatically generate heuristics for the dynamic workflow scheduling problem, with the goal of minimizing the VM rental fees and SLA penalties. Experiments have been conducted to evaluate the performance of the proposed approach. Compared with several existing heuristics and conventional Genetic Programming (GP) approaches, the proposed Dynamic Workflow Scheduling Genetic Programming (DWSGP) has better performance and is highly adaptable to variations in cloud environment.",
• notes = "Also known as \cite{9504709}",

}

Genetic Programming entries for Yifan Yang Aaron Chen Hui Ma Mengjie Zhang Victoria Huang

Citations