Abstract

objects 289-43596 . We wish to estimate the sums !#" %$ &('*)+& , of the sizes of objects of a given color , from a sampled subset of objects. How should the sampling distribution be chosen in order to jointly control both the variance of the estimators - ./ and the number of samples taken? This problem is motivated from network measurement, in which the are the byte sizes of traffic flows reported by routers, and the are the common properties of the packet of the flow, e.g., source and destination IP address. In this paper we propose a sampling scheme that optimally controls the volume of the measurements, and the variance of unbiased usage estimates - 0/ , while retaining usage detail down to the finest level of granularity in the colors. We provide algorithms for dynamic control of sample volumes and evaluate them on flow data gathered from a commercial IP network. The algorithms are simple to implement and robust to variation in network conditions. The work reported here has been applied in the measurement infrastructure of the commercial IP network. To not have employed sampling would have entailed an order of magnitude greater capital expenditure to accommodate the measurement traffic and its processing.

Citations