Energy-Aware Dynamic Resource Allocation and Container Migration in Cloud Servers: A Co-evolution GPHH Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{falloon:2024:GECCO,
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author = "Mathew Falloon and Hui Ma and Aaron Chen",
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title = "{Energy-Aware} Dynamic Resource Allocation and
Container Migration in Cloud Servers: A Co-evolution
{GPHH} Approach",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and
Ying Bi and Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and
Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and
Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
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pages = "1219--1227",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, timetabling
and scheduling, co-evolution, combinatorial
optimization, Real World Applications",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654070",
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size = "9 pages",
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abstract = "Containers are a popular way of deploying software in
cloud data centers. Containers are allocated to Virtual
machines (VMs) which are allocated to Physical machines
(PMs) within the data center. Since the resources
required by containers often do not match those of VMs,
where to allocate them must be decided. A poor solution
can result in high energy costs. Many existing methods
to solve this problem use heuristics which do not
consider containers leaving the data center after being
allocated. Some do consider migrating containers
between VMs but few do for energy efficiency reasons.
These overlooked aspects may lead to increased energy
usage, particularly since studies have demonstrated
that many containers run for only a brief duration. In
this paper, we develop a model of the container-based
cloud resource allocation problem that considers the
energy impact of leaving and migrating containers. We
then design a new Genetic Programming Hyper-Heuristic
(GPHH) algorithm to jointly evolve three heuristics for
container placement, VM placement and container
migration control. We use newly designed terminals to
ensure the effectiveness of our GPHH algorithm.
Experiments have been conducted with results indicating
that the heuristics evolved by our GPHH algorithm can
achieve better performance compared to several
state-of-the-art techniques.",
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notes = "GECCO-2024 RWA A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Mathew Falloon
Hui Ma
Aaron Chen
Citations