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Edge resource slicing approaches for latency optimization in AI-edge orchestration

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Abstract

Edge service computing is an emerging paradigm for computing, storage, and communication services to optimize edge framework latency and cost based on mobile edge computing (MEC) devices. The devices are battery-enabled and have limited communication and computation resources. X consolidation is a major issue in distributed heterogeneous MEC orchestrations, where X represents the task scheduling/device selection/channel selection/offloading strategy. The network entities need to enhance network performance under uncertain circumstances for such orchestrations. Haphazard X consolidation leads to abnormal resource and energy usage, quality of service (QoS) and latency of the edge framework. However, this study concentrates on analysing the impact of reinforcement learning-based edge resource consolidation models. The models are classified according to functionality, including device resource management, service request allocation, device selection, and offloading types. Finally, the article discusses and highlights some unresolved challenges for further study on MEC orchestration to enhance offloading strategy and resource management, as well as device and channel selection efficiency.

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Funding

This work was supported in part by the Basic Science Research Programs of the Ministry of Education (Grant No. NRF-2018R1A2B6005105) and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. 2019R1A5A8080290).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science (for Women), Hyderabad, Telangana, India

    P. Keerthi Chandrika

  2. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, Korea

    M. S. Mekala

  3. RLRC for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38544, Korea

    M. S. Mekala

  4. Dept of Math and Computer Science, Brandon University, Brandon, Canada

    Gautam Srivastava

  5. Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan

    Gautam Srivastava

  6. Department of Computer Science and Math, Lebanese American University, Beirut, 1102, Lebanon

    Gautam Srivastava

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  1. P. Keerthi Chandrika
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  2. M. S. Mekala
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  3. Gautam Srivastava
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Contributions

Conceptualization: KP; Methodology: KP and MSM; Validation: MSM; Writing—Original Draft: KP; Writing—Review & Editing: MSM, and GS.

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Correspondence to M. S. Mekala or Gautam Srivastava.

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Chandrika, P.K., Mekala, M.S. & Srivastava, G. Edge resource slicing approaches for latency optimization in AI-edge orchestration. Cluster Comput 26, 1659–1683 (2023). https://doi.org/10.1007/s10586-022-03817-7

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