Learning-Based Task Offloading for Delay-Sensitive Applications in Dynamic Fog Networks

Fog computing has the potential to liberate the computation-intensive mobile devices by task offloading. In this paper, we propose an online learning based task offloading algorithm for delay-sensitive applications in dynamic fog networks, which combines with the Combinatorial Multi-Armed Bandits (C...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-11, Vol.68 (11), p.11399-11403
Main Authors: Wang, Kunlun, Tan, Youyu, Shao, Ziyu, Ci, Song, Yang, Yang
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Combinatorial analysis
combinatorial multi-armed bandits
Computation offloading
Computational modeling
Computer simulation
Computing costs
Delay
delay minimization
Delays
Electronic devices
Fog computing
Heuristic algorithms
Machine learning
Mobile computing
multi-armed bandit
Multi-armed bandit problems
Resource management
Task analysis
task offloading
Task scheduling
Time-varying systems
Vehicle dynamics
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Summary:Fog computing has the potential to liberate the computation-intensive mobile devices by task offloading. In this paper, we propose an online learning based task offloading algorithm for delay-sensitive applications in dynamic fog networks, which combines with the Combinatorial Multi-Armed Bandits (CMAB) framework. First, the proposed algorithm learns the sharing computing resources of fog nodes at a negligible computational cost. Then, we aim to minimize the task's offloading latency by jointly optimizing the task allocation decision and the spectrum scheduling. Finally, simulation results show that the proposed algorithm achieves much better delay performance than the traditional Upper Confidence Bound (UCB) algorithm and maintains ultra-low offloading delay in dynamic system state.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2943647