As a key 5G enabler, edge computing locates resources near users to enable low-latency services, and microservices architecture (MSA) decomposes monolithic services into microservices, easing deployment and scaling of mission-critical applications with high reliability requirements. Existing studies on microservice bac...
Yue Zeng, Jun-Long Zhou, Lin-Hui Wang et al.· IEEE transactions on compute...· 0 citations
Split Learning (SL) enables collaborative model training by distributing computation between resource-constrained mobile edge devices and a central server. In this distributed framework, mobile edge devices compute the early layers of the model and transmit feature maps to the server. However, due to practical mobile w...
Wen-Xuan Zhou, Zhi-Hao Qu, Baoliu Ye et al.· IEEE Transactions on Mobile...· 0 citations
A time-varying integer program to minimize the long-term total cost of the edge AI inference system, including the inference latency, the inference error rate, the query-dispatching communication cost, and the energy consumption, subject to resource and workload constraints is proposed.
Ming-Tao Ji, Hehan Zhao, Lei Jiao et al.· Science China Information Sc...· 0 citations
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