With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

随着通信技术的多样性和网络边缘计算资源的异构性，边缘云和对等设备（协作者）都可以被清除以为资源受限的物联网（IoT）设备提供计算资源。本文提出了一种新型的协同计算范式，充分利用了物联网设备，机会闲置的协作者和专用边缘云的计算资源。合作者分别在空闲/忙碌状态下提供计算/卸载帮助。考虑到协作者的信道随机性和机会性计算资源份额，我们研究了IoT设备的随机卸载控制，涉及本地处理，卸载到边缘云和协作者的计算量。该问题被公式化为有限水平马尔可夫决策问题，其目的是在满足硬计算截止期限约束的情况下，将IoT设备和协作者的预期总能耗降至最低。基于随机优化理论推导了最优卸载策略，表明通过协同计算可以将能源消耗按比例降低。通过更好的无线信道条件或协作者的更高计算能效，可以实现更多节能。与其他几种卸载方案相比，仿真结果验证了所提出策略的最优性以及终端设备与边缘云之间协同计算的效率。