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Intelligent Predicting Method for Optimizing Remote Loading Efficiency in Edge Service Migration

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Abstract

In mobile edge computing (MEC) systems, enhancing the learning capabilities of edge nodes through Artificial Intelligence (AI) can improve the efficiency of dynamically allocating resources. For the scenario of edge service migration, various tasks in lightweight IoT devices are offloaded to edge nodes and the services on edge nodes are migrated adaptively to another available node nearer to the users while they move. To speed up network application loading during service migration, this paper proposes an intelligent trace-driven predicting approach (ITPA) that improves the efficiency of I/O scheduling and the hit ratio of caching when migrating services between resource-constrained edge nodes. First, based on the characteristics of sequential access to the binary codes of an application during its startup progress, the request loading list is generated by tracing key I/O requests at that phase. Then, an intelligent algorithm is designed to search and select the key I/O requests in the loading list. Finally, the efficiency of data acquisition is improved by implementing a prefetch strategy for the client side and a three-level caching strategy for the server side. Experimental results show that the ITPA reduces the service startup time during stateless migration.

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References

  1. Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY (2020) Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal 7(8):7457–7469. https://doi.org/10.1109/JIOT.2020.2984887

    Article  Google Scholar 

  2. Ranjan A, Guim F, Chincholkar M, Ramchandran P, Mishra R, Ranganath S (2021) Convergence of edge services amp; edge infrastructure. In: 2021 IEEE conference on network function virtualization and software defined networks (NFV-SDN), pp 96–99. https://doi.org/10.1109/NFV-SDN53031.2021.9665021

  3. Liang Q, Shenoy P, Irwin D (2020) Ai on the edge: Characterizing ai-based iot applications using specialized edge architectures. In: 2020 IEEE International symposium on workload characterization (IISWC), pp 145–156. https://doi.org/10.1109/IISWC50251.2020.00023

  4. Li Y, Xu L (2019) The service computational resource management strategy based on edge-cloud collaboration. In: 2019 IEEE 10Th international conference on software engineering and service science (ICSESS), pp. 400–404. https://doi.org/10.1109/ICSESS47205.2019.9040830

  5. Peng F, Shou G, Hu Y, Liu Y (2018) Lightweight edge computing network architecture and network performance evaluation. In: 2018 Asia communications and photonics conference (ACP), pp 1–3. https://doi.org/10.1109/ACP.2018.8596139

  6. Zhou Z, Li X, Wang X, Liang Z, Sun G, Luo G (2020) Hardware-assisted service live migration in resource-limited edge computing systems. In: 2020 57Th ACM/IEEE design automation conference (DAC), pp 1–6. https://doi.org/10.1109/DAC18072.2020.9218677

  7. Ma L, Yi S, Carter N, Li Q (2019) Efficient live migration of edge services leveraging container layered storage. IEEE Trans Mob Comput 18(9):2020–2033. https://doi.org/10.1109/TMC.2018.2871842

    Article  Google Scholar 

  8. Lim E-J, Ahn S-Y, Kim Y-H, Cha G-I, Choi W (2017) Design of cache backend using remote memory for network file system. In: 2017 International conference on high performance computing simulation (HPCS), pp 864–869. https://doi.org/10.1109/HPCS.2017.131

  9. Dai D, Bao FS, Zhou J, Chen Y (2016) Block2vec: A deep learning strategy on mining block correlations in storage systems. In: 2016 45Th international conference on parallel processing workshops (ICPPW), pp 230–239. https://doi.org/10.1109/ICPPW.2016.43

  10. Liao J, Chen S (2016) Optimization of reading data via classified block access patterns in file systems. IEEE Access 4:9421–9427. https://doi.org/10.1109/ACCESS.2016.2642918

    Article  Google Scholar 

  11. Ma J, Zheng X, Liu Y, Chen Z (2021) Kbp: Mining block access pattern for i/o prediction with k-truss. In: 2021 IEEE Intl conf on parallel distributed processing with applications, big data cloud computing, sustainable computing communications, social computing networking, pp 167–176. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00035

  12. Zhang Y, Guo K, Ren J, Zhou Y, Wang J, Chen J (2017) Transparent computing: a promising network computing paradigm. Computing in Science Engineering 19(1):7–20. https://doi.org/10.1109/MCSE.2017.17

    Article  Google Scholar 

  13. Peng X, Ren J, She L, Zhang D, Li J, Zhang Y (2018) Boat: A block-streaming app execution scheme for lightweight iot devices. IEEE Internet of Things Journal 5(3):1816–1829. https://doi.org/10.1109/JIOT.2018.2812803

    Article  Google Scholar 

  14. Zhang D, Shen R, Ren J, Zhang Y (2018) Delay-optimal proactive service framework for block-stream as a service. IEEE Wireless Communications Letters 7(4):598–601. https://doi.org/10.1109/LWC.2018.2799935

    Article  Google Scholar 

  15. Cao K, Hu S, Shi Y, Colombo AW, Karnouskos S, Li X (2021) A survey on edge and edge-cloud computing assisted cyber-physical systems. IEEE Transactions on Industrial Informatics 17 (11):7806–7819. https://doi.org/10.1109/TII.2021.3073066

    Article  Google Scholar 

  16. Lee M, Eom YI (2020) Transaction-aware data cluster allocation scheme for qcow2-based virtual disks. In: 2020 IEEE International conference on big data and smart computing (bigcomp), pp 385–388. https://doi.org/10.1109/BigComp48618.2020.00-45

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Authors and Affiliations

  1. School of Software Engineering, Beijing Jiaotong University, No.3 Shangyuancun Haidian District, Beijing, 100044, Beijing, China

    Xianyu Meng & Wei Lu

  2. School of Engineering, Kitami Institute of Technology, Koencho, Kitami, 0900015, Hokkaido, Japan

    Xun Shao & Hiroshi Masui

Authors
  1. Xianyu Meng
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  2. Xun Shao
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  3. Hiroshi Masui
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Correspondence to Xun Shao or Wei Lu.

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Meng, X., Shao, X., Masui, H. et al. Intelligent Predicting Method for Optimizing Remote Loading Efficiency in Edge Service Migration. Mobile Netw Appl 27, 2218–2231 (2022). https://doi.org/10.1007/s11036-022-02002-9

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