An Efficient Bandit Learning based Online Task Offloading in Fog Computing-enabled Systems

초록

Fog computing technology has developed to support delay-sensitive applications by offering shared and adaptable communication, computation, and storage resources along the cloud-to-things continuum in Internet of Things (IoT) and cyber-physical systems (CPS). In order to minimize the delay of every task, task nodes (TNs) with computation-intensive and delay-sensitive tasks should have efficient strategies to select the optimal helper nodes (HNs) having spare computation resources for task offloading operations. However, the dynamic nature of fog computing environment characterized by the time varying change of HN computing resources as well as various types of tasks with different quality of service (QoS requirements) impose as inherent challenges for designing the efficient task offloading algorithms. To deal with these challenges, we apply the principle of multi-armed bandit (MAB) learning method to efficiently learn the uncertainty of fog computing environment. In particularly, we use Thomson sampling (TS) technique to acquire better exploitation and exploration trade-off, allowing TNs to select their corresponding HNs efficiently. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the.-greedy and UCB based offloading algorithms.