Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Chen:2022:SCC,
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author = "Yuheng Chen and Tao Shi and Hui Ma and Gang Chen2",
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title = "Automatically Design Heuristics for Multi-Objective
Location-Aware Service Brokering in Multi-Cloud",
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booktitle = "2022 IEEE International Conference on Services
Computing (SCC)",
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year = "2022",
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pages = "206--214",
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month = "10-16 " # jul,
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address = "Barcelona, Spain",
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keywords = "genetic algorithms, genetic programming, Costs,
Service computing, Quality of service, QoS, Dynamic
scheduling, Dynamic programming, Task analysis,
Multi-objective optimization, multi-cloud,
location-aware, service brokering, GPHH",
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ISSN = "2474-2473",
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DOI = "doi:10.1109/SCC55611.2022.00039",
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size = "9 pages",
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abstract = "Multi-cloud provides cloud services at distributed
locations. As the number of cloud services from
multi-cloud providers growing, how to select proper
cloud services to optimize multiple potentially
conflicting objectives simultaneously has become a
challenging task. Multi-objective location-aware
service brokering (MOLSB) aims to provide a set of
trade-off solutions to minimize cost and latency. To
handle dynamic resource requirements, various
heuristics have been proposed to efficiently find
suitable cloud services. However, these heuristics
cannot achieve consistently good performance on a wide
range of problem instances. Additionally, instead of
replying on a single heuristic, it is desirable to
design a set of effective heuristics that can balance
different objectives with varied trade-offs. Genetic
Programming hyper-heuristics (GPHH) have been applied
to automatically design heuristics for many
multi-objective dynamic optimization problems, e.g.,
workflow scheduling. In this pa-per, we propose a new
GPHH-based approach, named GPHH-MOLSB, to automatically
generate a group of Pareto-optimal heuristics that can
be used to satisfy varied QoS preferences. GPHH-MOLSB
can significantly outperform several existing
approaches based on evaluation on real-world
datasets.",
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notes = "Also known as \cite{9860215}",
- }
Genetic Programming entries for
Yuheng Chen
Tao Shi
Hui Ma
Aaron Chen
Citations