TY - JOUR
T1 - How Matching Theory Enables Multi-access Edge Computing Adaptive Task Scheduling in IIoT
AU - Chi, Jiancheng
AU - Xu, Chao
AU - Qiu, Tie
AU - Jin, Di
AU - Ning, Zhaolong
AU - Daneshmand, Mahmoud
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Fifth-generation mobile communication technology (5G) is a powerful driving force for the Industrial Internet of Things (IIoT). In the 5G-based IIoT, multi-access edge computing (MEC) can move traffic and service computing from the centralized cloud to the edge networks, thus, effectively improving the real-time performance of task processing. In this context, it is crucial to assign real-time tasks generated by numerous edge devices to MEC servers. Existing schemes usually schedule tasks in batches within time slots and ignore the situations where edge tasks arrive with time-varying density. However, the problem is that these schemes can lead to extra waiting delay in the slots with sparse tasks, thus, resulting in additional latency in task processing. To solve this problem, we propose a task scheduling scheme based on two-stage hybrid matching. The proposed scheme measures the time-varying density of tasks and switches between two stages: offline and online matching stages, according to the different task densities. Experimental results show that our scheme has a lower task execution time compared with other state-of-the-art schemes.
AB - Fifth-generation mobile communication technology (5G) is a powerful driving force for the Industrial Internet of Things (IIoT). In the 5G-based IIoT, multi-access edge computing (MEC) can move traffic and service computing from the centralized cloud to the edge networks, thus, effectively improving the real-time performance of task processing. In this context, it is crucial to assign real-time tasks generated by numerous edge devices to MEC servers. Existing schemes usually schedule tasks in batches within time slots and ignore the situations where edge tasks arrive with time-varying density. However, the problem is that these schemes can lead to extra waiting delay in the slots with sparse tasks, thus, resulting in additional latency in task processing. To solve this problem, we propose a task scheduling scheme based on two-stage hybrid matching. The proposed scheme measures the time-varying density of tasks and switches between two stages: offline and online matching stages, according to the different task densities. Experimental results show that our scheme has a lower task execution time compared with other state-of-the-art schemes.
UR - http://www.scopus.com/inward/record.url?scp=85136846129&partnerID=8YFLogxK
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U2 - 10.1109/MNET.122.2100762
DO - 10.1109/MNET.122.2100762
M3 - Article
AN - SCOPUS:85136846129
SN - 0890-8044
VL - 37
SP - 126
EP - 131
JO - IEEE Network
JF - IEEE Network
IS - 3
ER -