Model-based reinforcement learning for router port queue configurations

作     者：Ajay Kattepur Sushanth David Swarup Kumar Mohalik 

作者机构：Intelligence System GroupEricsson ResearchBangalore 560093India Ericsson Managed Services Unit in TexasPlanoTX 75025USA 

出 版 物：《Intelligent and Converged Networks》 (智能与融合网络（英文）)

年 卷 期：2021年第2卷第3期

页      面：177-197页

核心收录：

学科分类：0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0839[工学-网络空间安全] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：router port queues model-based Reinforcement Learning(RL) network slicing 

摘      要：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.