Adaptive random sampling for traffic load measurement

Traffic measurement and monitoring is an important component of network QoS management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement are not only desirable, but increasingly becoming a necessity. In this paper, we propose and analyze an adaptive random packet sampling technique for traffic load measurement. In particular, we address the problem of bounding sampling error within a prespecified tolerance level. We derive a relationship between the number of packet samples, the accuracy of load estimation and the squared coefficient of variation of packet size distribution. Based on this relationship, we propose a sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples, yet simple to implement.