基于深度强化学习的软件定义安全中台QoS实时优化算法

Deep reinforcement learning based algorithm for real-time QoS optimization of software-defined security middle platform

针对软件定义安全场景中的服务质量(QoS)实时优化方案因安全防护手段与业务场景不匹配而导致的适用困难和性能下降的问题,提出了基于深度强化学习的软件定义安全中台QoS实时优化算法。首先,将碎片化的安全需求与安全基础设施统一到软件定义安全中台云模型中;然后,通过深度强化学习结合云计算技术提高安全中台的实时匹配和动态适应能力;最后,生成满足QoS目标的安全中台资源实时调度策略。实验结果表明,与现有实时算法相比,所提算法不但保证负载均衡,还提高了18.7%的作业调度成功率以提高服务质量,降低了34.2%的平均响应时间,具有很好的稳健性,更适用于实时环境。

To overcome the problem that the real-time optimization of the quality of service(QoS)in software-defined security scenarios was hindered by the mismatch between security protection measures and business scenarios,which led to difficulties in application and performance degradation.,a novel algorithm based on deep reinforcement learning for optimizing QoS in software defined security middle platforms(SDSmp)in real-time was proposed.Firstly,the fragmented security requirements and infrastructure were integrated into the SDSmp cloud model.Then by leveraging the power of deep reinforcement learning and cloud computing technology,the real-time matching and dynamic adaptation capabilities of the security middle platform were enhanced.Finally,a real-time scheduling strategy for security middle platform resources that meet QoS goals was generated.Experimental results demonstrate that compared to existing real-time methods,the proposed algorithm not only ensures load balancing but also improves job success rate by 18.7%for high QoS and reduces the average response time by 34.2%,and it is highly robust and better suited for real-time environments than existing methods.