Sampling biases in network path measurements and what to do about it
Srikanth Kandula, Ratul Mahajan · 2009
We show that currently prevalent practices for network path measurements can produce inaccurate inferences because of sampling biases. Theinferredmeanpathlatencycanbemorethanafactorof two off the truemean. Wepresentthe Broomtoolkit thathasthree methods to correct for this bias. Broom places no burden on the measurementprocessitselfandcanbeappliedposthoctoanymeasured data set. Our evaluation finds that two of the methods are particularly effective. One of them estimatesmissing path samples byembeddingthenodesinalow-dimensionalcoordinatespace.For realistic sampling rates, the quality of its estimatesfor path latency approximatesideal, unbiasedsampling. The othermethodisbased on a view of network paths as being composed of source-specific, destination-specific, and shared components. It reduces bias for a widerangeofpathproperties,suchaslatency,hopcountandcapacity. Applying Broomtodatafromarealmeasurementstudyleadsto substantialchangesintheresultinginferences. Forsomenetworks, thepost-correctionestimateis30%higherthantheoriginal.