Learning-Based URLLC-Aware Task Offloading for Internet of Health Things

In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (G...

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Bibliographic Details
Published inIEEE journal on selected areas in communications Vol. 39; no. 2; pp. 396 - 410
Main Authors Zhou, Zhenyu, Wang, Zhao, Yu, Haijun, Liao, Haijun, Mumtaz, Shahid, Oliveira, Luis, Frascolla, Valerio
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
adversarial multi-armed bandit
Algorithms
Computation offloading
Delays
Edge computing
Energy consumption
EXP3
Extreme values
Internet
Internet of Health Things
Machine learning
Multi-armed bandit problems
Network latency
Optimization
Performance measurement
Probability
Reliability
Servers
Statistical analysis
Task analysis
task offloading
URLLC
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Summary:In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency communication (URLLC) constraints have imposed new challenges for task offloading optimization. In this article, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability, occurrence probability of extreme events, and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose a URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. URLLC awareness is achieved by dynamically balancing the URLLC constraint deficits and energy consumption through online learning. We provide a rigorous theoretical analysis to show that guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. Finally, the effectiveness and reliability of UTO-EXP3 are validated through simulation results.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3020680