L10 - Traffic Monitoring Estimation and Engineering Nick...

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Traffic: Monitoring, Estimation, and Engineering Nick Feamster CS 7260 February 14, 2007
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2 Administrivia Syllabus redux More time for traffic monitoring/engineering Simulation vs. emulation pushed back (Feb. 21) Workshop deadlines (6-page papers) Reducing unwanted traffic: April 17 Large scale attacks: April 21 Network management: April 26 Include in your proposal whether you will aim for one of these.
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3 End-to-End Routing Behavior Prevalence: Likelihood of seeing a route Most paths dominated by a single prevalent route Persistence: Likelihood that a route stays same Persistence of routes was variable 2/3 of paths had routes persisting for days or weeks Observed doubling in pathologies over the course of a year. Major Findings Characterization study. We should be asking why these observations occur and whether they may have changed.
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4 Method Centralized controller launches distributed traceroutes Pairwise traceroutes across sites First dataset has interval of 1-2 days Second dataset has some measurements in bursts Second dataset has paired measurements (Mostly) poisson distribution of observations across paths PASTA principle: fraction of observations implies fraction of overall time
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5 Arguing “Representativeness” Always tricky business… This paper: fraction of ASes traversed by the pairwise paths (8% “cross section”) D1: ~ 7k traceroutes; D2: ~38k traceroutes Assigning pathologies to a particular AS is challenging No explanation of why or where . Centralized controller limits flexibility Traceroute issues Limitations
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6 Routing Loops Loops: about 0.1% of all observations Two modes: under three hours, more than 12 hours Loops come in clusters Loops can affect nearby routers 5 observations of multiple AS loops (how can this happen? Examples…) 1 1 3
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7 Erroneous Routing Packets clearly taking wrong path ( e.g. , through Israel) One example of erroneous routing
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8 Changing Paths Connectivity altered mid-stream Between 0.16% and 0.44% Recovery times bimodal Cause Fluttering Rapidly oscillating routing Load balance/splitting Distinct from fluttering caused by routing oscillations?
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9 Failures and Unreachability Availability rate of infrastructure about 99.5% - 99.8% (about 2.5 “nines”) Assumes representative measurements Assumes that other times paths were actually usable Neglects times when infrastructure could not drive the measurement Most path lengths: about 15-16 hops Some diurnal patterns Small number of ASes responsible for most of outages
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Routing Stability: Prevalence Do routes change often? Are they stable over time?
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L10 - Traffic Monitoring Estimation and Engineering Nick...

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