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CS 268: Lecture 14 Internet Measurements

CS 268: Lecture 14 Internet Measurements. Scott Shenker and Ion Stoica Computer Science Division Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1776. Project Presentations. Five slides Five minutes Five questions

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CS 268: Lecture 14 Internet Measurements

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  1. CS 268: Lecture 14Internet Measurements Scott Shenker and Ion Stoica Computer Science Division Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1776

  2. Project Presentations • Five slides • Five minutes • Five questions • You’re outta there!

  3. 1st slide: Title • 2nd slide: motivations and problem formulation • Why is the problem important? • What is challenging/hard about your problem • 3rd slide: main idea of your solution • 4th slide: status • 5th slide: future plans and schedule

  4. Poll • How many people know: • Lamport clocks • Timestamp vectors • Byzantine agreement • Epidemic/gossip dissemination algorithms • Consistency • Bayou

  5. Internet Measurements • This field went from casual sideline to serious science with Vern Paxson’s thesis in 1997 • Now there is a massive literature in Internet measurements • Two devoted conferences (PAM and IMC) in addition to standard networking conferences

  6. Today’s Lecture • Provide a quick overview of the kinds of techniques used and questions addressed • Tom Anderson (UW) will lead us in a design exercise

  7. Philosophical Question • Why do we take Internet measurements?

  8. Measurement Techniques • Passive: • Traces • Netflow data • Network telescopes • BGP looking glass sites • …… • Active: • Ping • Traceroutes • ……

  9. Measurement Infrastructure • NIMI • Planetlab • Telescopes (CIED) • Traceroute servers • …..

  10. Varieties of Measurement Studies • Basic characterization from direct measurements • Raw statistics • Model fitting • Just because it is “direct” doesn’t mean it is easy! • Look at the care taken by Paxson in the two readings • Deep inference • Estimating something that can’t be directly measured

  11. Examples of Direct Studies • Packet sizes and protocol type • Flow sizes • Route stability • DNS usage • Stationarity • …….

  12. A Few Results • Most flows are small, but most bytes in large flows • Most flows are slow, but most bytes are in fast flows • Rate/size highly correlated (not due to slow start) • Losses are reasonably modeled as Poisson arrival of loss events followed by geometric loss train • Internet routing does not yield good paths…. In 2003, • Bytes: TCP 83% UDP 16% • Packets: TCP 75% UDP 22% • Flows: TCP 56% UDP 33%

  13. Traffic Variances • A Bellcore team discovered that ethernet traffic had strage behavior • No matter what time scale they averaged it over, the variances were still large! • How could that be?

  14. Self Similarity, LRD, and All That…. • Correlation: r(t) = < x(s+t)x(s)> - <x(s)>2 • Short-range correlation: Sum r(t) finite • Long-range correlation (LRD): Sum r(t) infinite • Example of LRD: r(t) ~ t-.5

  15. Aggregates and Variance • Consider a sum of M iid random variables: • Variance of sums ~ 1/M • Consider process with short-range correlations: • On large-enough time scales, intervals are iid • Variance should decay as 1/M • Only with LRD can the variance decay more slowly • Often as a power law with p<1

  16. LRD Comes from Many Sources? • Poisson arrivals, with long-tailed sizes is one mechanism • Most files small • Most bytes in large files • Pareto distributions are very common!

  17. Zipf and Pareto • Zipf: F(rank) ~ rank-b • F is frequency, size, etc. • 1 < b • Pareto: P(size) ~ size-a • 1 < a < 2 • Log(size) has geometric distribution! • Pareto distributed interarrival times means logs are uncorrelated • a = 1+1/b and b = 1/(a-1) • The tail of the distribution can’t be ignored!

  18. Examples of Zipf/Pareto • Income distribution • Asteroid sizes • Letter frequencies • File sizes • Telnet packet interarrivals • Web access frequencies (impact on caching) • AS connectivity

  19. Examples of Inference Tools • Bandwidth estimation • Critical path analysis • Network tomography • Flow rate causes • …..

  20. Bandwidth Estimation (I) • Send pair of packets back-to-back • If not intermediate packets intervene, their spacing is a function of the bandwidth of the bottleneck link • Send a series of packet-pairs, measure the minimal delays • Doesn’t work well: often overestimate capacity! • Can do better with many packets back-to-back

  21. Bandwidth Estimation (II) • Send variable sized packets, k hops (using TTL). • Delay at link i: Di = Ai + P/Ci • Ai is size-independent delay • P is packet size • Ci is capacity of link • Model delay of k hops: Sum (i = 1 to k) Di

  22. Possible Causes of Flow Rates • Application: doesn’t generate data fast enough • Opportunity: never leaves slow-start • Receiver: limited dby receive window • Sender: limited by sending buffer • Bandwidth: using full bottleneck bandwidth • Congestion: flow is responding to packet loss • Transport: none of the above…

  23. Differentiating Causes • Take trace of flow • Estimate RTT • Look at window epochs • Identify where TCP is in cycle

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