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Found 3 papers, 1 papers with code
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing.
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network.
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.
