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.
@article{arxiv.1806.10547,
title = {Online optimal task offloading with one-bit feedback},
author = {Shangshu Zhao and Zhaowei Zhu and Fuqian Yang and Xiliang Luo},
journal= {arXiv preprint arXiv:1806.10547},
year = {2018}
}
Comments
We have submitted this paper to GlobalSIP 2018 on Jun. 29th