Aggregation of statistical data from passive probes: Techniques and best practices
Résumé
Passive probes continuously generate statistics on large number of metrics, that are possibly represented as probability mass functions (pmf). The need for consolidation of several pmfs arises in two contexts, namely: (i) whenever a central point collects and aggregates measurement of multiple disjoint vantage points, and (ii) whenever a local measurement processed at a single vantage point needs to be distributed over multiple cores of the same physical probe, in order to cope with growing link capacity.
In this work, we take an experimental approach and study both cases using, whenever possible, open source software and datasets. Considering different consolidation strategies, we assess their accuracy in estimating pmf deciles (from the 10th to the 90th) of diverse metrics, obtaining general design and tuning guidelines. In our dataset, we find that Monotonic Spline Interpolation over a larger set of percentiles (e.g., adding 5th, 10th, 15th, and so on) allow fairly accurate pmf consolidation in both the multiple vantage points (median error is about 1%, maximum 30%) and local processes (median 0.1%, maximum 1%) cases.