A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment

Jinpeng Li; Jiale Zhao; Peng Chen; Yunni Xia; Fan Li; Yin Li; Feng Zeng; Hui Liu

doi:10.1007/978-3-031-44836-2_1

A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment

Jinpeng Li, Jiale Zhao^*, Peng Chen, Yunni Xia, Fan Li, Yin Li, Feng Zeng, Hui Liu

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects.

Original language	English
Title of host publication	Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings
Editors	Yuchao Zhang, Liang-Jie Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-17
Number of pages	15
ISBN (Print)	9783031448355
DOIs	https://doi.org/10.1007/978-3-031-44836-2_1
Publication status	Published - 2023
Event	30th International Conference on Web Services, ICWS 2023 - Honolulu, United States Duration: 23 Sept 2023 → 26 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14209 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Web Services, ICWS 2023
Country/Territory	United States
City	Honolulu
Period	23/09/23 → 26/09/23

Keywords

Edge computing
Genetic algorithm
Multi-armed bandits learning
Service caching
Thompson sampling

Access to Document

10.1007/978-3-031-44836-2_1

Cite this

Li, J., Zhao, J., Chen, P., Xia, Y., Li, F., Li, Y., Zeng, F., & Liu, H. (2023). A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment. In Y. Zhang, & L.-J. Zhang (Eds.), Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings (pp. 3-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14209 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44836-2_1

Li, Jinpeng ; Zhao, Jiale ; Chen, Peng et al. / A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment. Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings. editor / Yuchao Zhang ; Liang-Jie Zhang. Springer Science and Business Media Deutschland GmbH, 2023. pp. 3-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b46bab562d27433aac059de0681bcd38,

title = "A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment",

abstract = "Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects.",

keywords = "Edge computing, Genetic algorithm, Multi-armed bandits learning, Service caching, Thompson sampling",

author = "Jinpeng Li and Jiale Zhao and Peng Chen and Yunni Xia and Fan Li and Yin Li and Feng Zeng and Hui Liu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 30th International Conference on Web Services, ICWS 2023 ; Conference date: 23-09-2023 Through 26-09-2023",

year = "2023",

doi = "10.1007/978-3-031-44836-2_1",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Li, J, Zhao, J, Chen, P, Xia, Y, Li, F, Li, Y, Zeng, F & Liu, H 2023, A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment. in Y Zhang & L-J Zhang (eds), Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14209 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 3-17, 30th International Conference on Web Services, ICWS 2023, Honolulu, United States, 23/09/23. https://doi.org/10.1007/978-3-031-44836-2_1

A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment. / Li, Jinpeng; Zhao, Jiale; Chen, Peng et al.
Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings. ed. / Yuchao Zhang; Liang-Jie Zhang. Springer Science and Business Media Deutschland GmbH, 2023. p. 3-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14209 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Li, Yin

AU - Zeng, Feng

AU - Liu, Hui

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N2 - Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects.

AB - Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects.

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KW - Genetic algorithm

KW - Multi-armed bandits learning

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings

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T2 - 30th International Conference on Web Services, ICWS 2023

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Li J, Zhao J, Chen P, Xia Y, Li F, Li Y et al. A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment. In Zhang Y, Zhang LJ, editors, Web Services – ICWS 2023 - 30th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 3-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44836-2_1