Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs’ CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

中文翻译：

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基于契约理论和贝叶斯匹配博弈的分层多接入边缘计算计算卸载

多访问边缘计算 (MEC) 因其在计算密集型和延迟关键型应用程序中的良好性能而成为一种有前途的范例。然而，现有的边缘计算节点（ECN）仍然无法满足来自计算服务订阅者（CSS）的巨大计算请求。为了充分利用 MEC 网络的优势，在网络虚拟化 (NV) 场景下开发了分层计算卸载框架。因此，设计了一个两步顺序过程来刺激所提出的框架。在第一步中，提出了一种激励机制，其中可以由 MEC 运营商激励更多的临时 ECN，然后加入 MEC 网络。没有完善的 ECN 信息，最优合约项目（ECN' MEC 运营商和 ECN 之间的 CPU 贡献和奖励）可以通过考虑个体理性 (IR) 和激励兼容 (IC) 约束来实现。获得ECNs的CPU贡献后，第二步考虑ECNs和CSSs之间的计算资源分配问题。由于 CSS 具有私人信息，因此利用具有外部性的贝叶斯匹配游戏来对问题进行建模。而传统的面向居民的 Gale-Shapley (RGS) 算法无法保证稳定性。因此，开发了一种总能收敛到稳定结果的迭代匹配算法。最后，仿真结果表明，考虑到实际场景，我们提出的两步顺序决策过程可以显着提高社会福利，并具有合理的计算复杂度。