Detecting Top-k Flows Combining Probabilistic Sketch and Sliding Window

Detecting Top-k Flows Combining Probabilistic Sketch and Sliding Window

Blog Article

Efficient real-time top-k flows measurement plays a pivotal role in enhancing both network performance and security, including tasks such as timely traffic scheduling, optimizing network latency and identifying potential security threats.However, traditional methods for detecting top-k flows suffer from decreased accuracy and high memory overhead.Furthermore, many existing methods overlook finer-grained measurements, such as the detection within the latest short time intervals.With the Accessories increasing expansion scale and link speed of the network, an accurate real-time top-k flows identified method is required.

This paper proposes wSketch, a novel sketch-based method for real-time top-k flows detection.The innovations of wSketch are that it combines with the sliding window model and circular queue model, and introduces a novel probabilistic update solution.The probabilistic update mechanism gives the larger flow a greater chance of retention, the sliding window model focuses on the latest flow in the last $mathbb {W}$ time units, and the circular queue reduces memory consumption.Therefore, MOST150 Coaxial Mercedes Pre-Amp wSketch provides insights into the current network situation and does well in anticipating future trends.

The experimental results showcase wSketch’s superior performance, achieving over 96% accuracy with a small memory size of 20KB.