Per-Flow Traffic Measurement Through Randomized Counter Sharing

• Published in: IEEE/ACM transactions on networking Vol. 20; no. 5; pp. 1622 - 1634
• Main Authors: Li, Tao, Chen, Shigang, Ling, Yibei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Computer networks; Decoding; Estimation; Modules; Networks; Noise; On-line systems; Online; Radiation detectors; Random access memory; Size measurement; Static random access memory; Studies; Traffic engineering; Traffic flow; Vectors
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2012.2192447

Traffic measurement provides critical real-world data for service providers and network administrators to perform capacity planning, accounting and billing, anomaly detection, and service provision. One of the greatest challenges in designing an online measurement module is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. To meet this challenge, we should minimize the number of memory accesses per packet and implement the measurement module in the on-die SRAM. The small size of SRAM requires extremely compact data structures to be designed for storing per-flow information. The best existing work, called counter braids, requires more than 4 bits per flow and performs six or more memory accesses per packet. In this paper, we design a fast and compact measurement function that estimates the sizes of all flows. It achieves the optimal processing speed: two memory accesses per packet. In addition, it provides reasonable measurement accuracy in a tight space where the counter braids no longer work. Our design is based on a new data encoding/decoding scheme, called randomized counter sharing. This scheme allows us to mix per-flow information together in storage for compactness and, at the decoding time, separate the information of each flow through statistical removal of the error introduced during information mixing from other flows. The effectiveness of our online per-flow measurement approach is analyzed and confirmed through extensive experiments based on real network traffic traces. We also propose several methods to increase the estimation range of flow sizes.