Abstract
This chapter presents an efficient approach for origin–destination flow measurement in high-speed networks, where an origin–destination (OD) flow between two routers is the set of packets that pass both routers. The OD flow measurement has wide usage in many network management applications. We consider two performance metrics, measurement efficiency and accuracy. The former requires measurement functions to minimize per-packet processing overhead in order to keep up with the line speeds of today’s high-speed networks. The latter requires measurement functions to achieve accurate measurement results with small bias and standard deviation. We present a novel measurement method that employs a compact data structure for packet information storage and uses a new statistical inference approach for OD flow measurement. Both simulations and experiments are performed to demonstrate the effectiveness of our method. The rest of this chapter is organized as follows: Section 4.1 gives the problem statement and performance metrics. Section 4.2 presents a novel origin-destination flow measurement method. Section 4.3 discusses the simulation results. Section 4.4 presents the experimental results. Section 4.5 describes other related methods. Section 4.6 gives the summary.
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Notes
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The fisher information [13] is a way of measuring the amount of information that an observable random variable \(x\) carries about an unknown parameter \(\theta \) upon which the likelihood function of \(\theta \), \(L(\theta )=f(x;\theta )\), depends.
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Li, T., Chen, S. (2012). Origin–Destination Flow Measurement. In: Traffic Measurement on the Internet. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4851-8_4
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