1. Network Simulation and Testing Polly Huang EE NTU http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw

2. Traffic Papers • V. Paxson, and S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling. IEEE/ACM Transactions on Networking, Vol. 3 No. 3, pp. 226-244, June 1995 • W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, On the Self-Similar Nature of Ethernet Traffic. IEEE/ACM Transactions on Networking, Vol. 2, No. 1, pp. 1-15, Feb. 1995 • M. E. Crovella and A. Bestavros, Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes. IEEE/ACM Transactions on Networking, Vol 5, No. 6, pp. 835-846, December 1997 • Anja Feldmann; Anna C. Gilbert; Polly Huang; Walter Willinger, Dynamics of IP traffic: A study of the role of variability and the impact of control. In the Proceeding of SIGCOMM '99, Cambridge, Massachusetts, September 1999

3. Paper Selection

4. Identifying Internet Traffic Failure of Poisson Self-similar Traffic Practical Model

5. The Problem • What is the traffic workload like? • Call/packet arrival rate as a process • What kind of process is it? • Very old problem and a lot of work

6. Because • Traces are available • Researchers care about • The validness of their assumption • The network traffic being independent Poisson • Operation people care a lot about • The amount of buffer/bandwidth to provision for their networks • The profit comes from satisfying customers with minimum infrastructure cost

7. Telephone Network • Assumptions • Poisson call arrivals • Exponential call duration • Wonderful Property • Poisson mixing with Poisson is still Poisson • Average rate well-characterize a call • The whole queueing theory

8. Data Network? • Wide-Area Traffic: The Failure of Poisson Modeling • V. Paxson, and S. Floyd • IEEE/ACM Transactions on Networking, Vol. 3 No. 3, pp. 226-244, June 1995

9. A Study of the Wide-Area Traffic • Two units of examination • Connections vs. packets • A sizeable number of traces • ~4M connections, ~26M packets • Different location and different time • Inter-arrival processes • TCP connections • Telnet packets • FTPDATA connections • Going self-similar

10. Unit of Observation • Telephone network • Circuit-switched • The unit is circuit, i.e., a call • People picking up the phone and talk for a while • Data network • Packet-switched • The unit is packet • Another unit is connection, comparable to call • People starting up an FTP connection and send data for a while

11. Packet  Connection • Hosts send/receive packets over a channel at the transport layer • Reliable: TCP • Non-reliable: UDP • Packets from various channels multiplex at the the network layer • IP Routers switched on the packets

12. ? ? Inter-Arrival Process: A Little Exercise Beginning SYN ACK&SYN ACK&Segment 1 FIN ACK&FIN ACK End Beginning

13. TCP Connection Arrival Poisson? Depends

14. Application Dependent • TELNET • Users typing ‘telnet cc.ee.ntu.edu.tw’ • FTP • User typing ‘ftp cc.ee.ntu.edu.tw’ • FTPDATA burst • User typing ‘mget net-simtest-*.ppt’ • FTPDATA • Each individual TCP transfer • NNTP & SMTP • Machine initiated and/or timer-driven

15. Independent and Poisson?

16. Quick Summary • TELNET and FTP • Independent and Poisson • Both the 1-hour and 10-min scales • FTPDATA bursts and SMTP • At the 10-min interval • Not ‘terribly far’ from Poisson • SMTP inter-arrival is not independent • FTPDATA, NNTP • Clearly not Poisson

17. Before One Can Explain • Human-initiated process • Independent and Poisson • Non-human-initiated process • Well, who knows

18. Explanations I • TELNET and FTP • User initiated • Users typing ‘telnet cc.ee.ntu.edu.tw’ • User typing ‘ftp cc.ee.ntu.edu.tw’ • FTPDATA bursts • User typing ‘mget net-simtest-*.ppt’ • Actually, taking the closely-spaced connections… (<= 4 sec) • FTPDATA • TCP connections

19. Explanations II • NNTP • Flooding to propagate network news • Arrival of news trigger another • Periodical and implementation/configuration dependent • SMTP • Mailing list • Timer effects from the DNS queries

20. TELNET Packets Poisson? No, heavy-tailed!

21. Show in 4 Ways • Distribution of packet inter-arrival time • Exponential processes ramp up significantly slower • Packet arrival pattern in seconds and 10 seconds • Exponential processes are smoother at the 10sec scale • Variance-time plot • Change of variance to time scale • Var of exponential processes decays quickly • Packet arrival rate process in seconds • By the sole visual effect • Exponential processes are less spiky

22. Full TELNET model? Poisson connection arrival Heavy-tailed packet arrival within a connection

23. FTPDATA • Connection arrival is not Poisson • Clustered in bursts • Burst sizes in bytes is quite heavy-tailed • A 0.5% of bursts contribute to 50% of the traffic volume

24. OK. We know it’s not Poisson. But what?

25. Going Self-Similar • Well, since other evidences suggest so • And it’s the next good thing • Go straight into producing self-similar traffic

26. Producing Self-Similar Traffic • ON/OFF sources • Fix ON period rate • ON/OFF period length heavy-tailed • M/G/ • Customer arrival being Poisson • Service time being heavy-tailed with infinite variance • Authors’ own model • Pseudo-self-similar • Not long-range dependent though

27. Performance Implication • Low-priority traffic starvation • Shall the high-priority traffic being long-range dependent (bursty) • Admission control based on recent traffic failing • ‘Congestions haven’t happened for a long while’ does not mean it won’t happen now

28. The Real Message Poisson is no longer sufficient!

29. Identifying Internet Traffic Failure of Poisson Self-similar Traffic Practical Model

30. Self-Similar What? • On the Self-Similar Nature of Ethernet Traffic • Will E. Leland; Murad S. Taqqu; Walter Willinger; Daniel V. Wilson • IEEE/ACM Transactions on Networking, Vol. 2, No. 1, pp. 1-15, Feb. 1995

31. This One Easier • Self-similarity in World Wide Web Traffic: Evidence and Possible Causes • Mark E. Crovella; Azer Bestavros • IEEE/ACM Transactions on Networking, Vol 5, No. 6, pp. 835-846, December 1997

32. Self-Similar Process Serpgask Triangles

33. Definition • X: a stationary time series • X(m): the m-aggregates • Summing the time series over non-overlapping blocks of m • X is H-self-similar if • X (m) has the same distribution for all positive m

34. Same Distribution? • Same autocorrelation function • r(k) = E[(Xt - )(Xt+k - )]/2 • r(k) ~ k- • k   • 0 <  < 1

35. Significance of k- • Long-range dependence • Just another way of characterizing the same thing • Power-law decay • Slower than exponential decay • Therefore traffic does not smooth up •  < 1 • r(k) does not converge • Sum of r(k) infinite, I.e., variance infinite

36. Just FYI • The Hurst parameter: 1- /2

37. Tests for Self-Similarity • Variance-time plot • A line with slope - > -1 • R/S plot • Rescaled range grows as the number points included • A line with slope H an the log-log scale • Periodogram • Power spectrum to frequency • A line with slope  - 1 at the log-log scale • Whittle estimator • Confidence to a form • FGN or Fractional ARIMA

38. Pareto Review • Exponential • f(x) = ce-cx • Heavy-tailed • F(x) ~ x-c, 0 < c < 2 • Hyperbolic • Pareto • f(x) = ckc x-c-1 • F(x) = 1- (k/x)c • A line at the log-log scale of F(x) plot

39. In Addition to the Theory • A HUGE volume of Ethernet traces • Show consistency of being self-similar in all sorts of tests • Implication to traffic engineering • A bombshell!

40. Why Self-Similar? • Theory suggests • Fix rate ON/OFF process • Heavy-tailed length • Looking into the length • The ON time: transmission time • The OFF time: silent time

41. Physical Cause • Heavy-tailed transmission time • Heavy-tailed file sizes • Magic of the nature • E.g., book size in library

42. Identifying Internet Traffic Failure of Poisson Self-similar Traffic Practical Model

43. So, enough Math. Just tell me what to do! It depends!

44. Cutting to the Chase • The structural model • user level: Poisson arrival and heavy-tailed duration • network level: TCP closed-loop feedback control and ack clocking • Variability: delay and congestion • Let simulators track the complex behavior

45. Why not FGN? • IP Traffic Dynamics: The Role of Variability and Control • Anja Feldmann; Anna C. Gilbert; Polly Huang; Walter Willinger • In the Proceeding of SIGCOMM '99, Cambridge, Massachusetts, September 1999

46. Remember Wavelet Analysis? • FFT • Frequency decomposition • fj, Fourier coefficient • Amount of the signal in frequency j • WT: wavelet transform • Frequency (scale) and time decomposition • dj,k, wavelet coefficient • Amount of the signal in frequency j, time k

47. Self-similarity • Energy function • Ej = Σ(dj,k)2/Nj • Weighted average of the signal strength at scale j • Self-similar process • Ej = 2j(2H-1) C <- the magic!! • log2 Ej = (2H-1)j + log2C • linear relationship between log2 Ej andj

48. ?? RTT ‘Shape’ of Self-Similarity Self-similar

49. 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 8 0 0 8 8 Wavelet Example 1 0 -1 00 00 00 00 11 11 11 11 s1 s2 s3 s4 d1 d2 d3 d4

50. 10 00 00 00 10 00 00 00 s1 s2 s3 s4 d1 d2 d3 d4 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 1 2 0 Adding Periodicity • packets arrive periodically, 1 pkt/23 msec • coefficients cancel out at scale 4