Incentive Mechanism for Resource Trading in Video Analytic Services Using Reinforcement Learning

Nan He, Song Yang*, Fan Li, Liehuang Zhu, Lifeng Sun, Xu Chen, Xiaoming Fu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Video analytics play a pivotal role in enhancing the safety of intelligent surveillance and autonomous driving. However, the transmission of vast video data and the computational demands of video analytics present challenges within traditional cloud computing paradigms. To address latency concerns, dynamic video analytics often leverage edge deployments. Nevertheless, the efficient allocation of resources at the edge, balancing cost-effectiveness and accuracy, becomes crucial, especially when multiple video analytics services concurrently operate within the system. This paper introduces an edge-centric incentive mechanism designed to encourage greater participation from edge nodes in offloading tasks. The key focus is on addressing the dynamic nature of edge resources and optimizing system returns through a rational pricing mechanism. We propose a decentralized Soft Actor-Critic algorithm grounded in game theory (DSACG) to autonomously learn the optimal pricing strategy. A comprehensive theoretical analysis, supported by extensive simulations, substantiates the effectiveness of our proposed solution.

Original languageEnglish
Pages (from-to)3803-3816
Number of pages14
JournalIEEE Transactions on Services Computing
Volume17
Issue number6
DOIs
Publication statusPublished - 2024

Keywords

  • Resource allocation
  • deep reinforcement learning
  • game theory
  • pricing mechanism
  • video analytics

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He, N., Yang, S., Li, F., Zhu, L., Sun, L., Chen, X., & Fu, X. (2024). Incentive Mechanism for Resource Trading in Video Analytic Services Using Reinforcement Learning. IEEE Transactions on Services Computing, 17(6), 3803-3816. https://doi.org/10.1109/TSC.2024.3424220