C. Wang, B. Jia, H. Yu, X. Li, X. Wang and T. Taleb, "Deep Reinforcement Learning for Dependency-aware Microservice Deployment in Edge Computing," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5141-5146, doi: 10.1109/GLOBECOM48099.2022.10000818.
Deep reinforcement learning for dependency-aware microservice deployment in edge computing
|Author:||Wang, Chenyang1; Jia, Bosen1; Yu, Hao2;|
1College of Intelligence and Computing, Tianjin University, Tianjin, China
2Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
3School of Big Data & Software Engineering, Chongqing University, Chongqing, China
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301276073
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-27
Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm’s priorities in terms of average system cost and system reward.
|Pages:||5141 - 5146|
2022 IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, 4-8 December 2022 : proceedings
IEEE Global Communications Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was partially supported by the CHARITY project that received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016509. This work was also supported partially by the National Key Research and Development Program of China under Grant No. 2019YFB2101901; the National Science Foundation of China under Grant No. 62072332, China NSFC (Youth) through grant No. 62002260; and the China Postdoctoral Science Foundation under Grant No. 2020M670654, and the National NSFC (Grant No. 61902044), Key Research Program of Chongqing Science & Technology Commission (Grants No. cstc2021jscx-dxwtBX0019) and the Chinese Government Scholarship (NO. 202006250167) awarded by China Scholarship Council.
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