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Model-based reinforcement learning for router port queue configurations

Intelligent and Converged Networks 2021年第3期2卷 177-197页

作者： Ajay Kattepur Sushanth David Swarup Kumar Mohalik Intelligence System Group Ericsson ResearchBangalore 560093India Ericsson Managed Services Unit in Texas PlanoTX 75025USA

Fifth-generation(5G)systems have brought about new challenges toward ensuring Quality of Service(QoS)in differentiated *** includes low latency applications,scalable machine-to-machine communication,and enhanced mobile broadband *** order to satisfy these requirements,the concept of network slicing has been introduced to generate slices of the network with specific *** order to meet the requirements of network slices,routers and switches must be effectively configured to provide priority queue provisioning,resource contention management and *** routers from vendors,such as Ericsson,Cisco,and Juniper,have traditionally been an expert-driven process with static rules for individual flows,which are prone to sub optimal configurations with varying traffic *** this paper,we model the internal ingress and egress queues within routers via a queuing *** effects of changing queue configuration with respect to priority,weights,flow limits,and packet drops are studied in *** is used to train a model-based Reinforcement Learning(RL)algorithm to generate optimal policies for flow prioritization,fairness,and congestion *** efficacy of the RL policy output is demonstrated over scenarios involving ingress queue traffic policing,egress queue traffic shaping,and one-hop router coordinated traffic *** is evaluated over a real application use case,wherein a statically configured router proved sub optimal toward desired QoS *** automated configuration of routers and switches will be critical for multiple 5G deployments with varying flow requirements and traffic patterns.

关键词： router port queues model-based Reinforcement Learning(RL) network slicing

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