Online optimal task offloading with one-bit feedback

TL;DR

This paper introduces a multi-armed bandit approach for online task offloading in fog networks, utilizing one-bit feedback from helper nodes to optimize long-term happiness amid exploration-exploitation challenges.

Contribution

It proposes a novel UCB-type algorithm tailored for one-bit feedback in fog network task offloading, addressing the exploration-exploitation dilemma.

Findings

The algorithm effectively maximizes long-term happiness.

Numerical results validate the proposed strategy.

The approach adapts to different helper node preferences.

Abstract

Task offloading is an emerging technology in fog-enabled networks. It allows users to transmit tasks to neighbor fog nodes so as to utilize the computing resources of the networks. In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model. We consider the fact that different helper nodes prefer different kinds of tasks. Further, we assume each helper node just feeds back one-bit information to the task node to indicate the level of happiness. The key challenge of this problem lies in the exploration-exploitation tradeoff. We thus implement a UCB-type algorithm to maximize the long-term happiness metric. Numerical simulations are given in the end of the paper to corroborate our strategy.