A Genetic Programming Hyper-Heuristic Approach to Design High-Level Heuristics for Dynamic Workflow Scheduling in Cloud
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Escott:2020:SSCI,
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author = "Kirita-Rose Escott and Hui Ma and Gang Chen2",
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title = "A Genetic Programming Hyper-Heuristic Approach to
Design High-Level Heuristics for Dynamic Workflow
Scheduling in Cloud",
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booktitle = "2020 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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year = "2020",
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pages = "3141--3148",
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abstract = "Workflow scheduling in the cloud is the process of
allocating tasks to scarce cloud resources, with an
optimal goal. This is often achieved by adopting an
effective scheduling heuristic. Workflow scheduling in
cloud is challenging due to the dynamic nature of the
cloud, often existing works focus on static workflows,
ignoring this challenge. Existing heuristics, such as
MINMIN, focus mainly on one specific aspect of the
scheduling problem. High-level heuristics are
heuristics constructed from existing man-made
heuristics. In this paper, we introduce a new and more
realistic workflow scheduling problem that considers
different kinds of workflows, cloud resources and
high-level heuristics. We propose a High-Level
Heuristic Dynamic Workflow Scheduling Genetic
Programming (HLH-DSGP) algorithm to automatically
design high-level heuristics for workflow scheduling to
minimise the response time of dynamically arriving task
in a workflow. Our proposed HLH-DSGP can work
consistently well regardless of the size and pattern of
workflows, or number of available cloud resources. It
is evaluated using a popular benchmark dataset using
the popular WorkflowSim simulator. Our experiments show
that with high-level scheduling heuristics, designed by
HLH-DSGP, we can jointly use several well-known
heuristics to achieve a desirable balance among
multiple aspects of the scheduling problem
collectively, hence improving the scheduling
performance.",
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keywords = "genetic algorithms, genetic programming, Task
analysis, Dynamic scheduling, Cloud computing, Virtual
machining, Heuristic algorithms, Time factors, Dynamic
programming, Cloud Computing, Dynamic Workflow
Scheduling",
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DOI = "doi:10.1109/SSCI47803.2020.9308261",
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month = dec,
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notes = "Also known as \cite{9308261}",
- }
Genetic Programming entries for
Kirita-Rose Escott
Hui Ma
Aaron Chen
Citations