Online Learning based Matching for Decentralized Task Offloading in Fog-enabled IoT Systems

Matching theory has been applied to design efficient offloading solutions to the multi-task multi-helper (MTMH) problem in the fog computing networks, which is modeled 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 online learning based matching (OLM) algorithm for decentralized task offloading to reduce the offloading delay in Fog-enabled IoT Systems. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the epsilon-greedy and UCB based offloading algorithms.