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Optoelectronic Task Processing based on Multilevel Collaborative Computing Networks: An Optimization-based Approach

Published: 18 April 2024 Publication History

Abstract

This paper explores the problem of optoelectronic task processing based on multilevel collaborative computing networks. The core keywords of the research include optoelectronic task processing, edge computing, cloud computing, and computational offloading. The aim of the article is to investigate how to efficiently process optoelectronic tasks in a multilevel collaborative computing network through an optimization approach. We propose a distribution-based algorithm to solve the minimum overhead of optoelectronic task processing. Further, we propose an entropy decision-based algorithm to improve existing algorithms to avoid falling into local optimal solutions.

References

[1]
S. Yuan, J. Li, Y. Zhu, C. Wu, and Y. Ding, "An energy-efficient computing offloading framework for blockchain-enabled video streaming systems," in IEEE Global Communications Conference. ieee, 2022, pp. 5183-5188.
[2]
S. Yuan, J. Li, H. Chen, Z. Han, C. Wu, and Y. Zhang, "Jira: Joint incentive design and resource allocation for edge-based real-time video streaming systems," IEEE Transactions on Wireless Communications, 2022.
[3]
X. Xu, Q. Wu, L. Qi, W. Dou, S.-B. Tsai, and M. Z. A. Bhuiyan, "Trust-aware service offloading for video surveillance in edge computing enabled internet of vehicles," IEEE Transactions on Intelligent Trans- portation Systems, vol. 22, no. 3, pp. 1787-1796, 2012. 2021.
[4]
A. Sacco, F. Esposito, G. Marchetto, and P. Montuschi, "Sustainable task offloading in uav networks via multi-agent reinforcement learning," IEEE Transactions on Vehicular Technology, vol. 70, pp. 5003-5015, 5 2021.
[5]
S. Yuan, J. Li, J. Liang, Y. Zhu, X. Yu, J. Chen, and C. Wu, "Sharding for blockchain based mobile edge computing system: a deep reinforcement learning approach," in 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021, pp. 1-6.
[6]
S. Yuan, J. Li, C. Wu, Y. Ji, and Y. Zhang, "Dcvp: Distributed collaborative video stream processing in edge computing," in 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), 2020, pp. 625-632.
[7]
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. PieterAbbeel, and I. Mordatch, "Multi-agent actor-critic for mixed cooperative-competitive environ- ments," Advances in neural information processing systems, vol. 30, 2017.
[8]
S. Yuan, J. Li, and C. Wu, "Jora: Blockchain-based efficient joint computing offloading and resource allocation for edge video streaming systems," Journal of Systems Architecture, vol. 133, p. 102740, 2022.
[9]
Yan Ding, Kenli Li, Chubo Liu, and Keqin Li, "A Potential Game Theoretic Approach to Computation Offloading Strategy Optimization in End-Edge-Cloud Computing," IEEE Transactions on parallel and distributed systems, Vol. 33, No. 6, June 2022.
[10]
Liu Zening, Li Kai, Wu Liantao, Wang Zhi, and YangYang, "CATS: Cost Aware Task Scheduling in Multi-Tier Computing Networks, " Journal of Computer Research and Development, 57(9): 1810-1822, 2020.
[11]
Xiantao Jiang, F. Richard Yu, Tian Song, and Victor C.M. Leung, Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles with Edge Computing, IEEE Internet of Things Journal, 9 (16), pp. 14260-14272, August 2022.
[12]
Yuan S, Dong B, Lvy H, Adaptive Incentivize for Cross-Silo Federated Learning in IIoT: A Multi-agent Reinforcement Learning Approach[J]. IEEE Internet of Things Journal, 2023.
[13]
S. Yuan "TradeFL: A Trading Mechanism for Cross-Silo Federated Learning," 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), Hong Kong, Hong Kong, 2023, pp. 920-930.
[14]
H. Liu “Adaptive Processing for Video Streaming with Energy Constraint: A Multi-Agent Reinforcement Learning Method,” 2023 IEEE Global Communications Conference (Globecom), Kuala Lumpur, Malaysia, 2023.

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ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security
December 2023
363 pages
ISBN:9798400707964
DOI:10.1145/3638782
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 18 April 2024

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Author Tags

  1. Optoelectronic task processing
  2. cloud computing
  3. computational offloading
  4. edge computing

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