Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Yang:2021:CEC,
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author = "Yifan Yang and Gang Chen2 and Hui Ma and
Mengjie Zhang and Victoria Huang",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Budget and {SLA} Aware Dynamic Workflow Scheduling in
Cloud Computing with Heterogeneous Resources",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "2141--2148",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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keywords = "genetic algorithms, genetic programming, Cloud
computing, Schedules, Data centers, Processor
scheduling, Heuristic algorithms, Computational
modeling, dynamic workflow scheduling, cloud
computing",
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isbn13 = "978-1-7281-8393-0",
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DOI = "doi:10.1109/CEC45853.2021.9504709",
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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.",
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notes = "Also known as \cite{9504709}",
- }
Genetic Programming entries for
Yifan Yang
Aaron Chen
Hui Ma
Mengjie Zhang
Victoria Huang
Citations