Stochastic learning for opportunistic peer-to-peer computation offloading in IoT edge computing

Abstract

Peer-to-peer computation offloading has been a promising approach that enables resource-limited Internet of Things (IoT) devices to offload their computation-intensive tasks to idle peer devices in proximity. Different from dedicated servers, the spare computation resources offered by peer devices are random and intermittent, which affects the offloading performance. The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions. In this work, we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals, dynamic interuser interference, and opportunistic availability of peer devices. Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion, which takes into consideration its energy consumption, task delay, and task loss due to buffer overflow. The dynamic decision process among multiple requestors is formulated as a stochastic game. By constructing the post-decision states, a decentralized online offloading algorithm is proposed, where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations. Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms, especially in the scenarios with large task arrival probability or small helper availability probability.