Online Learning and Optimization for Computation Offloading in D2D Edge Computing and Networks

This paper introduces a framework of device-to-device edge computing and networks (D2D-ECN), a new paradigm for computation offloading and data processing with a group of resource-rich devices towards collaborative optimization between communication and computation. However, the computation process of task intensive applications would be interrupted when capacity-limited battery energy run out. In order to tackle this issue, the D2D-ECN with energy harvesting technology is applied to provide a green computation network and guarantee service continuity. Specifically, we design a reinforcement learning framework in a point-to-point offloading system to overcome challenges of the dynamic nature and uncertainty of renewable energy, channel state and task generation rates. Furthermore, to cope with high-dimensionality and continuous-valued action of the offloading system with multiple cooperating devices, we propose an online approach based on Lyapunov optimization for computation offloading and resource management without priori energy and network information. Numerical results demonstrate that our proposed scheme can reduce system operation cost with low task execution time in D2D-ECN.

Automated summary

This paper introduces a framework of device-to-device edge computing and networks (D2D-ECN), which enables collaborative optimization between communication and computation by utilizing resource-rich devices. To address the issue of computation interruption in task intensive applications, the D2D-ECN is equipped with energy harvesting technology to provide a green computation network and ensure service continuity. Reinforcement learning and Lyapunov optimization techniques are employed to overcome the challenges posed by renewable energy, channel state, and task generation rates.