TAO: A real-time network traffic analysis task orchestration framework with optimized filtering and scheduling

Abstract

Efficient real-time network traffic analysis is vital for ensuring security and operational effectiveness. Existing traffic analysis frameworks, including holistic and fully decoupled designs, struggle to provide both optimal logical reuse and fine-grained resource allocation, resulting in inefficiencies. To address these challenges, we introduce TAO, a high-performance task orchestration framework that merges the benefits of holistic and decoupled designs for real-time network traffic analysis. TAO separates analysis targets from processing logic, facilitating flexible task scheduling and optimized resource allocation. By generating directed acyclic task graph, TAO minimizes forwarding of shared traffic and employs an innovative packet filtering optimization method using statistical features from a prioritized tree. Additionally, we develop a heuristic scheduling approach that leverages pipeline-based scheduling to achieve comprehensive congestion control. Experimental results show that under 10 Gbps trace replay and 40 Gbps real-world traffic, TAO reduces resource consumption by up to 55% in the lab and 48% in deployment compared with baseline methods. These findings underscore TAO’s potential to significantly enhance the efficiency and scalability of network traffic processing frameworks in high-throughput environments.