Deep reinforcement learning based algorithm for real-time QoS optimization of software-defined security middle platform - Yuancheng LI; Yongtai QIN (Tongxin xuebao)
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.