Learn and Pick Right Nodes to Offload

TL;DR

This paper proposes an online task offloading strategy for fog computing that minimizes long-term latency by addressing non-stationary system parameters using a novel bandit model-based algorithm.

Contribution

It introduces a new algorithm for non-stationary bandit-based task offloading, proven to be asymptotically optimal in dynamic fog networks.

Findings

Algorithm achieves asymptotic optimality.

Numerical simulations validate the effectiveness.

Addresses abrupt changes in system parameters.

Abstract

Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a stochastic programming problem, where the expectations of the system parameters change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. We then put forward an effective algorithm to solve this challenging stochastic programming under the non-stationary bandit model. We further prove that our proposed algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical simulations are carried out to corroborate our designs.