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An optimized environment-adaptive computation offloading strategy for real-time cross-camera task in edge computing networks

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Abstract

With the large-scale establishment of cross-camera networks, edge computing plays an important role in real-time tasks with its abundant edge resources and flexible task offloading strategy. Conventional studies usually utilize cross-camera network topology and real-time task status to generate subtask offloading strategies. However, most existed approaches focus on utilizing static environment information to generate a fixed offloading strategy for single-target optimization, while dynamic environment information and joint optimization objectives are often ignored. In this paper, we model the computing process of cross-camera tasks as a Markov Decision Process (MDP) integrating spatiotemporal correlation, to make full use of the dynamic environment information in the edge computing network. In addition, to achieve multi-objective optimization of cross-camera tasks, this paper develops a joint Q learning equation that integrates multiple utility indicators and proposes a Deep Spatio-Temporal Q Learning (Deep-STQL) algorithm to solve the equation. Based on the camera frame rate and cross-camera task frame rate, a large number of experimental data show that our proposed Deep-STQL algorithm has significantly improved the convergence, hit rate, average processing delay, drop rate of subtask and computing load of real-time cross-camera tasks compared with the baselines.

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Some data, models and code generated or used during the study will be avaiable under reasonable request from the corresponding author.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62272100, and in part by the Consulting Project of Chinese Academy of Engineering under Grant 2023-XY-09, the Fundamental Research Funds for the Central Universities and the Academy-Locality Cooperation Project of Chinese Academy of Engineering under Grant JS2021ZT05.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Peng Yang, Meng Yi, Bing Li & Yuankang Sun

  2. Key Laboratory of Computer Network and Information Integration(Southeast University), Ministry of Education, Nanjing, China

    Peng Yang, Siming Jiang, Meng Yi, Bing Li, Yuankang Sun & Ruochen Ma

  3. School of Software Engineering, Southeast University, Nanjing, China

    Siming Jiang

  4. School of Cyber Science and Engineering, Southeast University, Nanjing, China

    Ruochen Ma

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  1. Peng Yang
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Correspondence to Peng Yang.

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Yang, P., Jiang, S., Yi, M. et al. An optimized environment-adaptive computation offloading strategy for real-time cross-camera task in edge computing networks. Multimed Tools Appl 83, 17251–17279 (2024). https://doi.org/10.1007/s11042-023-16102-5

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