TY - JOUR
T1 - CoRaiS
T2 - Lightweight Real-Time Scheduler for Multiedge Cooperative Computing
AU - Hu, Yujiao
AU - Jia, Qingmin
AU - Chen, Jinchao
AU - Yao, Yuan
AU - Pan, Yan
AU - Xie, Renchao
AU - Yu, F. Richard
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024
Y1 - 2024
N2 - Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.
AB - Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.
KW - Deep learning
KW - edge computing
KW - multiedge cooperative computing
KW - real-time scheduling
UR - http://www.scopus.com/inward/record.url?scp=85193493488&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2024.3402257
DO - 10.1109/JIOT.2024.3402257
M3 - 文章
AN - SCOPUS:85193493488
SN - 2327-4662
VL - 11
SP - 28649
EP - 28666
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 17
ER -