Algorithms and estimators for summarization of unaggregated data streams

• Statistical summaries and estimators for unaggregated data streams are proposed.
• Applications include summarizing IP traffic.
• We develop algorithms for collecting more informative summaries.
• We propose estimators for domain queries and flow-size distribution.

Statistical summaries of IP traffic are at the heart of network operation and are used to recover aggregate information on subpopulations of flows. It is therefore of great importance to collect the most accurate and informative summaries given the router's resource constraints. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.