An Efficient Bandit Learning for Matching based Distributed Task Offloading in Fog Computing Network

초록

To seek for an efficient distributed offloading solution to the multi-task multi-helper (MTMH) problem in the fog computing networks, we model it as a matching game between a set of task nodes (TNs) having task computation needs and a set of helper nodes (HNs) having available computing resources. However, the uncertainty of computing resource availability of HNs as well as dynamics of QoS requirements of tasks result in the lack of preferences of TN side that mainly poses a critical challenge to obtain a stable and reliable matching outcome. To address this challenge, we apply a multi-armed bandit (MAB) learning using Thomson sampling (TS) mechanism to acquire better exploitation and exploration trade-off, allowing TNs to match with their corresponding HNs efficiently. Based on these, this paper proposes an efficient bandit learning based matching (BLM) for distributed task offloading in the fog computing networks. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the epsilon-greedy and UCB based offloading algorithms.