To fully exploit the computational resources in different fog nodes, task offloading is emerging. In this work, under the fog computing scenario, an efficient online task offloading strategy is investigated to minimize the long-term cost of task offloading. To achieve this goal, the problem is modeled as a stochastic optimization problem. Moreover, the system parameters are characterized by random variables, and their expectations may change abruptly at unknown time slot. Besides, the information about the system parameters is only available through the feedbacks after the task finishes. Using the non-stationary multi-armed bandit framework, we propose an efficient algorithm to handle this challenging stochastic programming. Furthermore, theoretical analyses are presented to prove the asymptotic optimality of the proposed algorithm. Numerical results reveal the advantages of this algorithm.
ZHU Zhaowei
,
LIU Ting
,
QIAN Hua
,
LUO Xiliang
. Online task offloading in non-stationary fog-enabled networks[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(5)
: 699
-707
.
DOI: 10.7523/j.issn.2095-6134.2020.05.015
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