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End-to-End Performance-based Autonomous VNF Placement with adopted Reinforcement Learning
2020
IEEE Transactions on Cognitive Communications and Networking
The autonomous placement of Virtual Network Functions (VNFs) is a key aspect of Zero-touch network and Service Management (ZSM) in Fifth Generation (5G) networking. Therefore, current orchestration frameworks need to be enhanced, accordingly. To address this need, this work presents an Adapted REinforcement Learning VNF Performance Prediction module for Autonomous VNF Placement, namely AREL3P. Our solution design bears a dual novelty. First, it leverages end-to-end service-level performance
doi:10.1109/tccn.2020.2988486
fatcat:2y7m6j2tkfb3do7bpmek3s5dgi