Recent growth of the Internet in both scale and complexity has imposed a number of difficult challenges on existing measurement techniques and approaches, which
are essential for both network management and many ongoing research projects. For
any measurement algorithm, achieving both accuracy and scalability is very challenging given hard resource constraints (e.g., bandwidth, delay, physical memory, and
CPU speed). My dissertation research tackles this problem by first proposing a novel
mechanism called residual sampling, which intentionally introduces a predetermined
amount of bias into the measurement process. We show that such biased sampling
can be extremely scalable; moreover, we develop residual estimation algorithms that
can unbiasedly recover the original information from the sampled data. Utilizing
these results, we further develop two versions of the residual sampling mechanism:
a continuous version for characterizing the user lifetime distribution in large-scale
peer-to-peer networks and a discrete version for monitoring flow statistics (including
per-flow counts and the flow size distribution) in high-speed Internet routers. For the
former application in P2P networks, this work presents two methods: ResIDual-based
Estimator (RIDE), which takes single-point snapshots of the system and assumes
systems with stationary arrivals, and Uniform RIDE (U-RIDE), which takes multiple snapshots and adapts to systems with arbitrary (including non-stationary) arrival
processes. For the latter application in traffic monitoring, we introduce Discrete
RIDE (D-RIDE), which allows one to sample each flow with a geometric random variable. Our numerous simulations and experiments with P2P networks and real
Internet traces confirm that these algorithms are able to make accurate estimation
about the monitored metrics and simultaneously meet the requirements of hard resource constraints. These results show that residual sampling indeed provides an ideal
solution to balancing between accuracy and scalability.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-2391 |
| Date | 2009 August 1900 |
| Creators | Wang, Xiaoming |
| Contributors | Loguinov, Dmitri |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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