Deep reinforcement learning for dependency-aware microservice deployment in edge computing
Wang, Chenyang; Jia, Bosen; Yu, Hao; Li, Xiuhua; Wang, Xiaofei; Taleb, Tarik (2023-01-11)
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.
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https://urn.fi/URN:NBN:fi-fe202301276073
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Abstract
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.
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