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Immersive Multimedia Service Caching in Edge Cloud with Renewable Energy

Published: 08 March 2024 Publication History

Abstract

Immersive service caching, based on the intelligent edge cloud, can meet delay-sensitive service requirements. Although numerous service caching solutions for edge clouds have been designed, they have not been well explored. Moreover, to the best of our knowledge, there is no work to consider the immersive service caching scheme under the supply of renewable energy. In this article, we investigate the service caching problem under the renewable energy supply to minimize service latency while making full use of renewable energy. Specifically, we formulate the service caching and renewable energy harvesting problem, which considers the dynamic renewable energy, unknown service requests, and limited capacity of the edge cloud. To solve this problem, we propose an effective algorithm, called OSCRE. Our algorithm first uses Lyapunov optimization to convert the time-average problem into time-independence optimization and thus realizes optimal renewable energy harvesting. Then, it realizes the service caching scheme using data-driven combinatorial multi-armed bandit learning. The simulation results show that the OSCRE scheme can save service latency while making sufficient use of renewable energy.

References

[1]
Kazi Masudul Alam, Abu Saleh Md Mahfujur Rahman, and Abdulmotaleb El Saddik. 2013. Mobile haptic e-book system to support 3D immersive reading in ubiquitous environments. ACM Trans. Multimedia Comput. Commun. Appl. 9, 4, Article 27 (Aug.2013), 20 pages.
[2]
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47, 2 (2002), 235–256.
[3]
Lixing Chen, Jie Xu, Shaolei Ren, and Pan Zhou. 2018. Spatio–temporal edge service placement: A bandit learning approach. IEEE Trans. Wireless Commun. 17, 12 (2018), 8388–8401.
[4]
Penglin Dai, Zihua Hang, Kai Liu, Xiao Wu, Huanlai Xing, Zhaofei Yu, and Victor Chung Sing Lee. 2020. Multi-armed bandit learning for computation-intensive services in MEC-empowered vehicular networks. IEEE Trans. Vehic. Technol. 69, 7 (2020), 7821–7834.
[5]
Mian Guo, Qirui Li, Zhiping Peng, Xiushan Liu, and Delong Cui. 2022. Energy harvesting computation offloading game towards minimizing delay for mobile edge computing. Comput. Netw. 204 (2022), 108678.
[6]
Yixue Hao, Min Chen, Hamid Gharavi, Yin Zhang, and Kai Hwang. 2020. Deep reinforcement learning for edge service placement in softwarized industrial cyber-physical system. IEEE Trans. Industr. Inf. 17, 8 (2020), 5552–5561.
[7]
Yixue Hao, Min Chen, Long Hu, M. Shamim Hossain, and Ahmed Ghoneim. 2018. Energy efficient task caching and offloading for mobile edge computing. IEEE Access (2018), 11365–11373.
[8]
F Maxwell Harper and Joseph A. Konstan. 2015. The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 4 (2015), 1–19.
[9]
Yuna Jiang, Jiawen Kang, Dusit Niyato, Xiaohu Ge, Zehui Xiong, Chunyan Miao, and Xuemin Shen. 2023. Reliable distributed computing for metaverse: A hierarchical game-theoretic approach. IEEE Trans. Vehic. Technol. 72, 1 (2023), 1084–1100. DOI:
[10]
Conor Keighrey, Ronan Flynn, Siobhan Murray, and Niall Murray. 2021. A physiology-based QoE comparison of interactive augmented reality, virtual reality and tablet-based applications. IEEE Trans. Multimedia 23 (2021), 333–341.
[11]
Uman Khalid, Muhammad Shohibul Ulum, Ahmad Farooq, Trung Q. Duong, Octavia A. Dobre, and Hyundong Shin. 2023. Quantum semantic communications for metaverse: principles and challenges. IEEE Wireless Commun. 30, 4 (2023), 26–36. DOI:
[12]
Meng-Lin Ku, Wei Li, Yan Chen, and K. J. Ray Liu. 2015. Advances in energy harvesting communications: Past, present, and future challenges. IEEE Commun. Surv. Tutor. 18, 2 (2015), 1384–1412.
[13]
Xiao Ma, Ao Zhou, Shan Zhang, and Shangguang Wang. 2020. Cooperative service caching and workload scheduling in mobile edge computing. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’20). IEEE, 2076–2085.
[14]
Yiming Miao, Yixue Hao, Min Chen, Hamid Gharavi, and Kai Hwang. 2020. Intelligent task caching in edge cloud via bandit learning. IEEE Trans. Netw. Sci. Eng. 8, 1 (2020), 625–637.
[15]
Minghui Min, Liang Xiao, Ye Chen, Peng Cheng, Di Wu, and Weihua Zhuang. 2019. Learning-based computation offloading for IoT devices with energy harvesting. IEEE Trans. Vehic. Technol. 68, 2 (2019), 1930–1941.
[16]
Omur Ozel, Kaya Tutuncuoglu, Jing Yang, Sennur Ulukus, and Aylin Yener. 2011. Transmission with energy harvesting nodes in fading wireless channels: Optimal policies. IEEE J. Select. Areas Commun. 29, 8 (2011), 1732–1743.
[17]
Konstantinos Poularakis, Jaime Llorca, Antonia M. Tulino, Ian Taylor, and Leandros Tassiulas. 2020. Service placement and request routing in MEC networks with storage, computation, and communication constraints. IEEE/ACM Trans. Netw. 28, 3 (2020), 1047–1060.
[18]
Samuel O. Somuyiwa, András György, and Deniz Gündüz. 2018. A reinforcement-learning approach to proactive caching in wireless networks. IEEE J. Select. Areas Commun. 36, 6 (2018), 1331–1344.
[19]
Chuan Sun, Xiuhua Li, Junhao Wen, Xiaofei Wang, Zhu Han, and Victor C. M. Leung. 2023. Federated deep reinforcement learning for recommendation-enabled edge caching in mobile edge-cloud computing networks. IEEE J. Select. Areas Commun. 41, 3 (2023), 690–705.
[20]
Sennur Ulukus, Aylin Yener, Elza Erkip, Osvaldo Simeone, Michele Zorzi, Pulkit Grover, and Kaibin Huang. 2015. Energy harvesting wireless communications: A review of recent advances. IEEE J. Select. Areas Commun. 33, 3 (2015), 360–381.
[21]
Xiaoyu Xia, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, and Hai Jin. 2020. Online collaborative data caching in edge computing. IEEE Trans. Parallel Distrib. Syst. 32, 2 (2020), 281–294.
[22]
Han Xiao, Changqiao Xu, Yunxiao Ma, Shujie Yang, Lujie Zhong, and Gabriel-Miro Muntean. 2021. Edge computing-assisted multimedia service energy optimization based on deep reinforcement learning. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’21). 1–6.
[23]
Jie Xu, Lixing Chen, and Shaolei Ren. 2017. Online learning for offloading and autoscaling in energy harvesting mobile edge computing. IEEE Trans. Cogn. Commun. Netw. 3, 3 (2017), 361–373.
[24]
Jie Xu, Lixing Chen, and Pan Zhou. 2018. Joint service caching and task offloading for mobile edge computing in dense networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18). IEEE, 207–215.
[25]
Zichuan Xu, Lizhen Zhou, Sid Chi-Kin Chau, Weifa Liang, Qiufen Xia, and Pan Zhou. 2020. Collaborate or separate? Distributed service caching in mobile edge clouds. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’20). IEEE, 2066–2075.
[26]
Shizhe Zang, Wei Bao, Phee Lep Yeoh, Branka Vucetic, and Yonghui Li. 2019. Filling two needs with one deed: Combo pricing plans for computing-intensive multimedia applications. IEEE J. Select. Areas Commun. 37, 7 (2019), 1518–1533.
[27]
Guanglin Zhang, Wenqian Zhang, Yu Cao, Demin Li, and Lin Wang. 2018. Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Trans. Industr. Inf. 14, 10 (2018), 4642–4655.
[28]
Jing Zhang, Jun Du, Yuan Shen, and Jian Wang. 2020. Dynamic computation offloading with energy harvesting devices: A hybrid-decision-based deep reinforcement learning approach. IEEE IoT J. 7, 10 (2020), 9303–9317.
[29]
Fengjun Zhao, Ying Chen, Yongchao Zhang, Zhiyong Liu, and Xin Chen. 2021. Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices. IEEE Trans. Netw. Serv. Manage. 18, 2 (2021), 2154–2165.

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    Published In

    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 20, Issue 6
    June 2024
    715 pages
    EISSN:1551-6865
    DOI:10.1145/3613638
    • Editor:
    • Abdulmotaleb El Saddik
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 March 2024
    Online AM: 31 January 2024
    Accepted: 25 January 2024
    Revised: 26 December 2023
    Received: 15 October 2023
    Published in TOMM Volume 20, Issue 6

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    Author Tags

    1. Edge cloud
    2. intelligent scheduling; renewable energy
    3. multimedia service caching

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    • King Saud University, Riyadh, Saudi Arabia

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