Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - QoS-Aware Cloud-Edge Collaborative Micro-Service Scheduling in the IIoT
AU - Peng, Kai
AU - Zhao, Bohai
AU - Bilal, Muhammad
AU - Xu, Xiaolong
AU - Nayyar, Anand
PY - 2023/6/30
Y1 - 2023/6/30
N2 - Benefiting from the substantial improvement of wireless sensor networks and embedded computing devices, the Industrial Internet of Things (IIoT), which is dedicated to alleviating computation barriers caused by hardware limitations, has been recommended for widespread deployment. In addition, as a decentralized collaboration mechanism, micro-services are being converted to be the preferred “collaborative” paradigm to enhance the working and management efficiency of the IIoT. However, IIoT applications, regarded as a primary generator of data, tend to be large-scale and time-sensitive, thus posing considerable challenges to IIoT networks in terms of improving quality-of-service (QoS) and the scheduling of micro-services. In view of these factors, this study attempts to construct a cloud-edge collaborative network architecture and to formulate total time consumption, resource utilization, and fairness as a mathematical model, while employing an offsite task placement mechanism to protect users' privacy. To this end, this study devised a QoS-aware microservice scheduling method named QCEMS. The results of extensive comparative experiments on two mutually exclusive scenarios with three different contrasting methods demonstrate that as the scale of micro-services or the number of containers increases, the proposed method can perform comparatively well on three pre-defined objectives (i.e., latency, resource utilization, and fairness) under privacy constraints.
AB - Benefiting from the substantial improvement of wireless sensor networks and embedded computing devices, the Industrial Internet of Things (IIoT), which is dedicated to alleviating computation barriers caused by hardware limitations, has been recommended for widespread deployment. In addition, as a decentralized collaboration mechanism, micro-services are being converted to be the preferred “collaborative” paradigm to enhance the working and management efficiency of the IIoT. However, IIoT applications, regarded as a primary generator of data, tend to be large-scale and time-sensitive, thus posing considerable challenges to IIoT networks in terms of improving quality-of-service (QoS) and the scheduling of micro-services. In view of these factors, this study attempts to construct a cloud-edge collaborative network architecture and to formulate total time consumption, resource utilization, and fairness as a mathematical model, while employing an offsite task placement mechanism to protect users' privacy. To this end, this study devised a QoS-aware microservice scheduling method named QCEMS. The results of extensive comparative experiments on two mutually exclusive scenarios with three different contrasting methods demonstrate that as the scale of micro-services or the number of containers increases, the proposed method can perform comparatively well on three pre-defined objectives (i.e., latency, resource utilization, and fairness) under privacy constraints.
KW - IIoT
KW - Micro-Service
KW - Cloud-Edge Collaborative
KW - QoS-Aware
U2 - 10.22967/HCIS.2023.13.028
DO - 10.22967/HCIS.2023.13.028
M3 - Journal article
VL - 13
JO - Human-centric Computing and Information Sciences
JF - Human-centric Computing and Information Sciences
SN - 2192-1962
M1 - 28
ER -