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Vehicle Edge Computing (VEC) is a novel computing paradigm that addresses the computational demands of intelligent vehicles by offloading tasks to edge servers. In a VEC environment, edge servers’ limited storage and processing capacity require a sensible task offloading strategy, where only a part of computing requirement can be offloaded directly to the VEC server and the remaining to the remote cloud. A primary challenge in this context is the creation of an effective and responsive task offloading algorithm that improves the utility. This study proposes an evolutionary game theoretic-based approach, utilizing a Dynamical-Resource Evolutionary Game (DREG) algorithm for decentralized task offloading. DREG leverages the Evolutionary Stable Strategy(ESS) and Adaptive Resource Allocation(ARA) method to optimize response delay and energy cost while increasing success rate. Experimental results indicate that DREG outperforms traditional methods across various performance metrics.

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An Evolutionary Game Theoretic-Based Approach to Task Offloading in Hybrid Vehicular Cloud-Edge Environment

  • 会议论文

  • 发表日期：

    2025年

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  • Jinpeng Li,
  • Yunni Xia,
  • Hui Liu,
  • Jiafeng Feng,
  • Ke Zhang,
  • Zhaoguang Ding,
  • Yumin Dong,
  • Yang Yu,
  • Yu Wang,
  • Qinglan Peng,
  • Xifeng Xu

摘要

Vehicle Edge Computing (VEC) is a novel computing paradigm that addresses the computational demands of intelligent vehicles by offloading tasks to edge servers. In a VEC environment, edge servers’ limited storage and processing capacity require a sensible task offloading strategy, where only a part of computing requirement can be offloaded directly to the VEC server and the remaining to the remote cloud. A primary challenge in this context is the creation of an effective and responsive task offloading algorithm that improves the utility. This study proposes an evolutionary game theoretic-based approach, utilizing a Dynamical-Resource Evolutionary Game (DREG) algorithm for decentralized task offloading. DREG leverages the Evolutionary Stable Strategy(ESS) and Adaptive Resource Allocation(ARA) method to optimize response delay and energy cost while increasing success rate. Experimental results indicate that DREG outperforms traditional methods across various performance metrics.