1. Traffic Prediction on the Internet Anne Denton

2. Outline  Paper by Y. Baryshnikov, E. Coffman, D. Rubenstein and B. Yimwadsana  Solutions  Time-Series prediction  Our work for the KDD-cup 03

3. Time Series Prediction on the Internet By Y. Baryshnikov, E. Coffman, D. Rubenstein and B. Yimwadsana  Adjustment to “hot spots”  Avoiding degradation, even “denial of service”  Can “hot spots” be predicted?  Can predicted “hot spots” be avoided?

4. What are “hot spots”?  Exceptionally large numbers of requests  Spontaneous, short lifetime  “instant” ramp up in traffic Only valid on long time scales  Claim: time scale for increase larger than time scale to react Why does increase take time?  Passing on the word  How good does a predictor have to be? Cost of missing a “hot spot” higher than aggregate cost of false alarms (similar to hurricane)

5. Examples  Olympics (Nagano 98)  Soccer World Cup (98)  NASA (95)

6. What to do about “hot spots”?  “The Columbia Hotspot Rescue Service: A Research Plan” E. Coffman, P. Jelenkovic, J.Nieh, and D. Rubenstein  Approaches Deal ad hoc with high request Build a better network (expensive) Content delivery services  Caching  Extra bandwidth  Suggested solution: use available and underutilized resources

7. Hotspot Rescue Service  Server-based approach Requires additional resources from server when necessary Resources provided by other members of Hotspot Rescue Service  Peer-to-Peer approach Requires additional resources from client when necessary Caching

8. Four Phases  Prediction (see rest of presentation) Server-based: daemons P2P: plug-ins  Replication Server-based: replication of objects P2P: identified cached copies More advanced: redistribution of traffic load  Notification Modifications to DNS (Domain Name System) P2P system proactively announces hot objects and indicates alternative locations?  Termination

9. Tail of Distribution  Requests per 10-second time slot  X-axis: number of hits per time slot  Y-axis: probability that that number of hits will be exceeded

10. Time Scales  Prediction relies on correlation between values at different times  Auto correlation function  Predictability on time scales of 5-30 min

11. Prediction Algorithm  Standard problem Signal processing Econometrics  Internet traffic Particularly bursty  Simplest model Linear extrapolation

12. Structure of Prediction Algorithms  Traffic observation # of requests in time unit (t-1,t] Usually 1s  Prediction window Duration W p  0  Advance notice   Prediction at time t: Mapping of observations in [t-W p,t] to a number p t  0 of requests predicted in interval [t+, t++1] that is  units in the future

13. Linear Prediction  Linear Fit: Least squares linear fit p t = f t (t+) with f t (s) = a t s+b t Minimizing  Performance: O(W+T) W: Window size T: uptime duration  Problems Prediction window size must match burstiness parameters governing request flow

14. Results  Depends on properties of auto- correlation function

15. Conclusions of Paper  Build a load-based taxonomy of web server traffic  Depends on technological, sociological, and psychological factors  Look for quantification of basic patterns reflecting behavior Do we agree ???  Why cluster when we can classify!!

16. Our Approach  Normally time series prediction uses only data in that time series  We use similarity to other instances E.g., other web sites  Model-free  Weighted Nearest Neighbor approach  Problem: How integrate time?

17. Typical Nearest Neighbor Classification / Regression  R(A 1, …, A n, C) Attributes A i C class label (classification) or continuous variable (regression)  Based on distance function on A i K nearest neighbors Neighbors within a range Use kernel function to weight closer ones higher

18. Weighting of Attributes  Some attributes are more important than others  Apply scaling to space  Optimize weights through Hill-climbing Genetic Algorithm  How does this generalize to a time- series?

19. Our Answer  Identify “relevant” sections in the time series E.g. times with already high download rates  We’ll call each relevant section a “prediction”

20. Predictions  Each prediction contains information about The nature of the time series The time instance in question, i.e. the history of requests The actual change in requests  Make a table of predictions Leads to a relation just as standard classification / regression setting

21. Data Set  Paper citations in “e-print ArXive”  Background: KDD-cup 03 Predict the change in citations in successive 3- month periods Only consider periods with at least 6 citations Evaluation: L1 distance (Manhattan distance) between predicted and real difference  Very close match between citation history and request history Predict change in requests Only consider periods that already show large number of requests

22. Attributes of a “Prediction”  Quantitative attributes Number of citations in window Gradient of citations in window Aggregate number of citations up to and through window (assume finite time series)  Attribute values given by time series Keyword occurrences Author Number of revisions of papers Maximum time interval between revisions Country of origin Format

23. Similarity Function  Common kernel-function  What worked better

24. Plot of Similarity Function

25. Accuracy  No linear extrapolation data available Could lead to negative citations  Comparison Default prediction: No change: 1851 Very simple model (decrease by 0.3 in 3 months): 1532 Prediction based on average of time series (synchronized at first non-0): 1593 Prediction based on quantitative attributes: 1465 Full prediction (prelimiary): 1357 Weight optimized (very preliminary): reduction 1414 -> 1391

26. Results

27. Conclusions  Method works well for citation prediction  Yet to be tested for hot-spot prediction