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MP-DDPG: Optimal Latency-Energy Dynamic Offloading Scheme in Collaborative Cloud Networks

Published: 07 June 2023 Publication History

Abstract

Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still major hurdle. Collaborative edge and cloud computing are one of the solutions to this problem. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy by partitioning the task and offloading it to the collaborative cloud and edge network to reduce energy consumption. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.

References

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Xutao Chen, Haibo Ge, Linhuan Liu, Shun Li, Jiapeng Han, and Haiwen Gong. 2021. Computing Offloading Decision Based on DDPG Algorithm in Mobile Edge Computing. In 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 391--399.
[2]
Xianfu Chen, Honggang Zhang, Celimuge Wu, Shiwen Mao, Yusheng Ji, and Medhi Bennis. 2018. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet of Things Journal 6, 3 (2018), 4005--4018.
[3]
William L Cooper and Bharath Rangarajan. 2012. Performance guarantees for empirical markov decision processes with applications to multiperiod inventory models. Operations Research 60, 5 (2012), 1267--1281.
[4]
Thinh Quang Dinh, Jianhua Tang, Quang Duy La, and Tony QS Quek. 2017. Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications 65, 8 (2017), 3571--3584.
[5]
Honghao Gao, Xuejie Wang, Xiaojin Ma, Wei Wei, and Shahid Mumtaz. 2020. Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing. arXiv preprint arXiv:2012.05105 (2020).
[6]
Liang Huang, Xu Feng, Cheng Zhang, Liping Qian, and Yuan Wu. 2019. Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing. Digital Communications and Networks 5, 1 (2019), 10--17.
[7]
Yaqin Liu, Chubo Liu, Jing Liu, Yikun Hu, Kenli Li, and Keqin Li. 2022. Mobility-Aware and Code-Oriented Partitioning Computation Offloading in Multi-Access Edge Computing. Journal of Grid Computing 20, 2 (2022), 1--15.
[8]
Mahadev Satyanarayanan, Paramvir Bahl, Ramón Caceres, and Nigel Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing 8, 4 (2009), 14--23.
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Yunpeng Wang, Weiwei Fang, Yi Ding, and Naixue Xiong. 2021. Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach. Wireless Networks 27, 4 (2021), 2991--3006.
[10]
Hongxia Zhang, Yongjin Yang, Xingzhe Huang, Chao Fang, and Peiying Zhang. 2021. Ultra-low latency multi-task offloading in mobile edge computing. IEEE Access 9 (2021), 32569--32581.

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cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Publication History

Published: 07 June 2023

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Author Tags

  1. collaborative cloud computing
  2. computation offloading
  3. latency
  4. energy efficiency
  5. deep reinforcement learning
  6. multi-period deep deterministic policy gradient

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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