一种改进近端优化的多目标流QoS调度策略  

A Multi-objective Flow QoS Scheduling Strategy with Improved Proximal Optimization

作　　者：刘星彤 郑红[1] 黄建华[1] LIU Xingtong;ZHENG Hong;HUANG Jianhua(Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学计算机科学与工程系,上海200237

出　　处：《应用科学学报》2024年第3期499-512,共14页Journal of Applied Sciences

基　　金：上海市信息化发展(大数据发展)专项(No.201901043)资助。

摘　　要：软件定义网络可以搭载灵活的流调度策略来提升网络服务系统的服务质量,但随着业务流量复杂度的提升,现有的流调度算法会因场景匹配度的下降而导致性能受到影响。为此提出一种基于深度强化学习的智能路由策略。该策略通过软件定义网络收集各链路信息,基于长短期记忆网络与近端策略优化算法实现特征提取与状态感知,最终决策生成符合业务场景下服务质量(quality of service,QoS)目标的动态流量调度策略,并实现QoS最大化。实验结果表明,所提的方案与现有的路由策略相比可以使整套系统QoS指标提升7.06%,有效地提升了业务系统的吞吐率。Software-defined networking(SDN)can be equipped with flexible flow scheduling strategies to improve the quality of network service systems.However,as the complexity of business traffic increases,existing flow scheduling algorithms may suffer from performance degradation due to decreased scene matching.To address this problem,this paper proposes an intelligent routing strategy based on deep reinforcement learning.The strategy collects various link information through SDN,and implements feature extraction and state awareness based on long-short term memory networks and proximal policy optimization algorithms.The strategy generates dynamic flow scheduling strategies that meet quality of service(QoS)goals in business scenarios,thereby maximizing QoS.Experimental results show that the proposed scheme enhances the QoS index of the entire system by 7.06%compared to existing routing strategies,effectively improving the throughput of the business system.

关 键 词：深度强化学习 服务质量 路由优化 软件定义网络 在线调度 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]