软件定义网络(software-defined networks,SDN)流量调度提升网络性能和资源利用率、实现节能和负载均衡至关重要.传统的多目标优化算法在高流量和网络动态性增加的情况下显著影响算法的收敛速度,难以满足复杂网络环境的多样化需求.针对...软件定义网络(software-defined networks,SDN)流量调度提升网络性能和资源利用率、实现节能和负载均衡至关重要.传统的多目标优化算法在高流量和网络动态性增加的情况下显著影响算法的收敛速度,难以满足复杂网络环境的多样化需求.针对此问题,提出了一种基于深度强化学习的流量预测在线路由算法——OTPR-DRL:根据流量特征预测关键流和普通流,结合网络状态和流量信息建立线性规划问题获得关键流路由的最优解.为满足普通流不同服务质量(quality of service,QoS)需求,引入通用效用函数实现多目标优化,通过多智能体和优先级经验回放机制为普通流选择路由.实验结果表明,在高流量强度下,OTPR-DRL与现有的算法相比,提高了收敛速度,至少降低了10.26%的网络传输时延,3.09%的丢包率,提高了1.70%的吞吐率.展开更多
基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结...基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结SRv6在网络架构与性能、网络管理与运维以及新兴业务支撑等方面的应用,凸显了SRv6精细调度、灵活编程、服务融合等独特优势.与此同时,深入剖析SRv6在性能与效率、可靠性与安全性、部署与演进策略这3个方面所面临的关键挑战,并重点讨论当前主流的解决思路和发展趋势.最后,立足产业生态构建、人工智能引入、行业融合创新等视角,对SRv6未来的发展方向和挑战进行前瞻性思考和展望.研究成果将为运营商构建开放、智能、安全的新一代网络提供理论参考和实践指导.展开更多
为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案...为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案例验证了该策略在提升网络吞吐量、降低传输时延方面具有明显成效。展开更多
The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobil...The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods.展开更多
文摘软件定义网络(software-defined networks,SDN)流量调度提升网络性能和资源利用率、实现节能和负载均衡至关重要.传统的多目标优化算法在高流量和网络动态性增加的情况下显著影响算法的收敛速度,难以满足复杂网络环境的多样化需求.针对此问题,提出了一种基于深度强化学习的流量预测在线路由算法——OTPR-DRL:根据流量特征预测关键流和普通流,结合网络状态和流量信息建立线性规划问题获得关键流路由的最优解.为满足普通流不同服务质量(quality of service,QoS)需求,引入通用效用函数实现多目标优化,通过多智能体和优先级经验回放机制为普通流选择路由.实验结果表明,在高流量强度下,OTPR-DRL与现有的算法相比,提高了收敛速度,至少降低了10.26%的网络传输时延,3.09%的丢包率,提高了1.70%的吞吐率.
文摘基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结SRv6在网络架构与性能、网络管理与运维以及新兴业务支撑等方面的应用,凸显了SRv6精细调度、灵活编程、服务融合等独特优势.与此同时,深入剖析SRv6在性能与效率、可靠性与安全性、部署与演进策略这3个方面所面临的关键挑战,并重点讨论当前主流的解决思路和发展趋势.最后,立足产业生态构建、人工智能引入、行业融合创新等视角,对SRv6未来的发展方向和挑战进行前瞻性思考和展望.研究成果将为运营商构建开放、智能、安全的新一代网络提供理论参考和实践指导.
文摘为解决高密度无线局域网中接入拥塞、资源失衡及流量混传导致的性能瓶颈问题,从接入层面、资源层面及流量层面分析无线局域网接入拥塞问题,并从3个层面提出基于软件定义网络(Software Defined Network,SDN)流量调度的应对策略。实践案例验证了该策略在提升网络吞吐量、降低传输时延方面具有明显成效。
基金extend their appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors also extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under grant number RGP2/714/46.
文摘The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods.
