Adaptive IoT Service Configuration Optimization in Edge Networks
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021)(2021)
摘要
The collaboration of Internet of Things (IoT) devices promotes the computation at the network edge to satisfy latency-sensitive requests. The functionalities provided by IoT devices are encapsulated as IoT services, and the satisfaction of requests is reduced to the composition of services. Due to the hard-to-prediction of forthcoming requests, an adaptive service configuration is essential, when latency constraints are satisfied by composed services. This problem is formulated as a continuous time Markov decision process model constructed with updating system states, taking actions and assessing rewards constantly. A temporal-difference learning approach is developed to optimize the configuration, while taking long-term service latency and energy efficiency into consideration. Experimental results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configurations.
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关键词
adaptive IoT service configuration optimization,edge networks,Internet of Things devices,network edge,latency-sensitive requests,adaptive service configuration,latency constraints,continuous time Markov decision process model,temporal-difference learning approach,long-term service,close-to-optimal service configurations
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