Lizeth Patricia Aguirre Sanchez, Yao Shen, Minyi Guo
{"title":"DQS:在 SDN 中使用深度强化学习的 QoS 驱动型路由优化方法","authors":"Lizeth Patricia Aguirre Sanchez, Yao Shen, Minyi Guo","doi":"10.1016/j.jpdc.2024.104851","DOIUrl":null,"url":null,"abstract":"<div><p>In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2) high convergence times, and (3) congestion due to inadequate real-time traffic prioritization. To address these issues, this paper introduces a QoS-Driven Routing Optimization in Software-Defined Networking (SDN) using Deep Reinforcement Learning (DRL) to optimize routing and enhance QoS efficiency. Employing DRL, the proposed DQS optimizes routing decisions by intelligently distributing traffic, guided by a multi-objective function-driven DRL agent that considers both link and queue metrics. Despite the complexity of the network, DQS sustains scalability while significantly reducing convergence times. Through a Docker-based Openflow prototype, results highlight a substantial 20-30% reduction in end-to-end delay compared to baseline methods.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DQS: A QoS-driven routing optimization approach in SDN using deep reinforcement learning\",\"authors\":\"Lizeth Patricia Aguirre Sanchez, Yao Shen, Minyi Guo\",\"doi\":\"10.1016/j.jpdc.2024.104851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2) high convergence times, and (3) congestion due to inadequate real-time traffic prioritization. To address these issues, this paper introduces a QoS-Driven Routing Optimization in Software-Defined Networking (SDN) using Deep Reinforcement Learning (DRL) to optimize routing and enhance QoS efficiency. Employing DRL, the proposed DQS optimizes routing decisions by intelligently distributing traffic, guided by a multi-objective function-driven DRL agent that considers both link and queue metrics. Despite the complexity of the network, DQS sustains scalability while significantly reducing convergence times. Through a Docker-based Openflow prototype, results highlight a substantial 20-30% reduction in end-to-end delay compared to baseline methods.</p></div>\",\"PeriodicalId\":54775,\"journal\":{\"name\":\"Journal of Parallel and Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Parallel and Distributed Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0743731524000157\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Parallel and Distributed Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0743731524000157","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
DQS: A QoS-driven routing optimization approach in SDN using deep reinforcement learning
In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2) high convergence times, and (3) congestion due to inadequate real-time traffic prioritization. To address these issues, this paper introduces a QoS-Driven Routing Optimization in Software-Defined Networking (SDN) using Deep Reinforcement Learning (DRL) to optimize routing and enhance QoS efficiency. Employing DRL, the proposed DQS optimizes routing decisions by intelligently distributing traffic, guided by a multi-objective function-driven DRL agent that considers both link and queue metrics. Despite the complexity of the network, DQS sustains scalability while significantly reducing convergence times. Through a Docker-based Openflow prototype, results highlight a substantial 20-30% reduction in end-to-end delay compared to baseline methods.
期刊介绍:
This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing.
The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.