Days In Green (DIG): Forecasting the life of a healthy service

1. Days In Green (DIG): Forecas1ng the life of a healthy service Vibhav Garg, Arun Kejariwal (@ativilambit, @arun_kejariwal) Capacity and Performance Engineering @ Twitter June 2014

2. #WorldCup [1] hBp://www.newsweek.com/twiBer-‐us-‐soccer-‐what-‐radio-‐was-‐baseball-‐256336 (June 2014) [1]

3. [1] hBp://www.telegraph.co.uk/technology/twiBer/10912738/Wimbledon-‐goes-‐digital-‐with-‐TwiBer.html (June 2014) [1] #Wimbledon2014

4. Internet trends •  Mobile-first q 25% of total web usage [1] q Mobile data traffic: 81%, accelerating growth [1] •  Real-time [1] hBp://www.kpcb.com/file/kpcb-‐internet-‐trends-‐2014 (May 2014) VG, AK 4 #Selfie

5. Capacity & Performance •  Organic growth q Over 255M monthly active users [1] •  Evolving product landscape •  Handle Peak Trafﬁc q Mobile Busy Hour Is 66% Higher Than Average Hour in 2013, 83% by 2018 [2] q Events [1] hBps://investor.twiBerinc.com/releasedetail.cfm?releaseid=843245 [2] hBp://www.cisco.com/c/en/us/solu1ons/collateral/service-‐provider/visual-‐networking-‐index-‐vni/white_paper_c11-‐520862.html VG, AK 5

6. Systema1c Capacity Planning •  Objectives q Check under-allocation §  Performance, Availability o  Adversely impact user experience q Check over-allocation §  Operational efﬁciency o  Adversely impacts bottom line q Check poor scalability •  Approaches q Reactive §  Adversely impact user experience q Proactive Poor UX Underu'liza'on VG, AK 6

7. Systema1c Capacity Planning (contd.) •  Non-trivial q Rapidly evolving product landscape §  Changes services’ performance proﬁle q Organic growth •  Scalable Approach q Service Oriented Architecture §  100s of services q Millions of metrics [1,2] q Automated [1] hBp://strata.oreilly.com/2013/09/how-‐twiBer-‐monitors-‐millions-‐of-‐1me-‐series.html [2] hBp://strataconf.com/strata2014/public/schedule/detail/32431 VG, AK 7

8. DIG: Days in Green •  Objective q Statistically determine the # of days for which a service is expected to stay healthy •  Methodology q Determine driving resource q Determine capacity threshold T q Generate a time series and forecast q DIG - # days before the service is expected to exceed T VG, AK 8 Time Driving Resource DIG T

9. DIG (contd.) •  Determining Capacity Thresholds q Service speciﬁc §  Driving resource differs q Load Test §  Canaries §  Replay production trafﬁc q Examples §  CPU at 70% §  Disk utilization at, 80% §  RPS at X requests/sec VG, AK 9 SLA T CPU Latency

10. DIG (contd.) •  Time Series Analysis q Data collection §  Granularity o  Daily •  Long term forecast o  Which value? •  Close to the daily peak but low standard deviation (σ) o  Assume 7 day seasonality §  Duration o  30-90 days q Model ﬁtting q Forecast VG, AK 10 Percen'le Dura'on Mean σ 100 (Max) 57.7 3.29 99 14.4 mins 54.7 2.49 95 72 mins 53.1 2.4

11. DIG (contd.) •  Model ﬁtting q Linear §  Captures trend well §  Does not ﬁt well for seasonal time series §  No weightage to recent data VG, AK 11 R2 = 0.56

12. DIG (contd.) •  Model ﬁtting q Polynomial §  Fits better than linear, not good for forecasting §  Seasonality unaware VG, AK 12 R2 = 0.62

13. DIG (contd.) •  Model fitting q Splines §  Widely used for curve fitting §  Tend to overfit data §  Not suitable for forecasting q Triple Exponential Smoothing (Holt Winters) §  Good for fit and forecasting §  Trend and seasonality modeled implicitly •  ARIMA VG, AK 13

14. ARIMA •  Auto-Regressive Integrated Moving Average q (p, d , q) q Explicitly models seasonality and trend q Applicable to non-stationary time series q  Worst Case degenerates to linear ﬁt Autoregressive component Moving Average component Moving Average order Integrated order Autoregressive order VG, AK 14

15. DIG (contd.) •  Model Fitting q  ARIMA in action §  Captures underlying trend §  Captures seasonality q  Are we good? Not quite! VG, AK 15 Forecast

16. •  Time Series Characteristics q  Anomalies §  Positive §  Negative VG, AK 16 Anomalies DIG (contd.)

17. Breakout •  Time series characteristics q Breakout §  Flavors o  Mean shift o  Ramp up §  Direction o  Positive, Negative DIG (contd.) VG, AK 17

18. •  Time series characteristics q  Seasonality breaks q  Various reasons (but not limited to) §  Daily deployments §  Changes in trafﬁc §  Collection issues Seasonality Breaks VG, AK 18 DIG (contd.)

19. VG, AK 19 •  Curve ﬁtting with ARIMA q Trend and seasonality aware q What does the DIG forecast look like? Trend 1 Trend 2 DIG (contd.) Trend 3 Anomaly T Breakout

20. DIG (contd.) •  ARIMA Forecast §  Not a good forecast because of multiple trends and anomalies §  Wide confidence band §  40 Days In Green with Confidence band of 10-40 VG, AK 20 95% confidence band T DIG

21. •  ARIMA Forecast with breakout(s) eliminated §  35 Days In Green with a Conﬁdence Band of 2-40 §  Limitations o  Wide conﬁdence band o  Susceptible to anomalies VG, AK 21 DIG (contd.) T DIG

22. •  ARIMA Forecast with Breakout and Anomaly eliminated §  25 Days In Green with a Conﬁdence Band of 2-40 §  Narrow conﬁdence band §  Improved Accuracy VG, AK 22 DIG (contd.) T DIG

23. •  DIG Comparison q With breakout and anomaly detection DIG (contd.) VG, AK 23 DIG T Raw Raw -‐ BO Raw – BO-‐ Anomaly

24. DIG (contd.) VG, AK 24 •  Discussion q Boundary conditions §  False seasonality T

25. DIG (contd.) •  Limitations q “Quality” of data: Poor forecasts VG, AK 25 T

26. •  Limitations q Idiosyncratic patterns: Poor forecasts q Ongoing work! VG, AK 26 DIG (contd.) T

27. DIG (contd.) VG, AK 27 •  Current Status – Deployed in Production q Hundreds of services q Fully automated for CPU, extending to other metrics q DR Compliance §  Combine data from multiple datacenters §  Detect services that are close to DR threshold •  Future Work q Utilization Based Allocation

28. DIG (contd.) VG, AK 28 •  Anomaly Detection q Algorithm developed in-house q Presented at USENIX HotCloud’14[1] [1] hBps://www.usenix.org/conference/hotcloud14/workshop-‐program/presenta1on/vallis

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105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466%Mean Shift: 105.466% DIG (contd.) VG, AK 29 •  Breakout Detection q Algorithm developed in-house q To be presented at Velocity New York’14 [1] [1] hBp://velocityconf.com/velocityny2014/public/schedule/detail/35485

30. Wrapping up & Lessons learned •  DIG: Days In Green q Proactively assess future health of a service q Modeling and forecasting: ARIMA q Anomaly and Breakout removal •  Modeling q Hard to get a stable time series §  Organic growth, New products, Behavioral aspect q Exploring advanced data cleansing techniques q Improve Breakout and Anomaly Detection VG, AK 30

31. Acknowledgements •  Piyush Kumar, Capacity Engineer •  Winston Lee, Capacity Engineer •  Owen Vallis Jr & Jordan Hochenbaum, Ex Interns •  Nicholas James, Intern •  Management team VG, AK 31

32. Join the Flock •  We are hiring!! q https://twitter.com/JoinTheFlock q https://twitter.com/jobs q Contact us: @ativilambit, @arun_kejariwal Like problem solving? Like challenges? Be at cujng Edge Make an impact VG, AK 32

Days In Green (DIG): Forecasting the life of a healthy service

Days In Green (DIG): Forecasting the life of a healthy service

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Days In Green (DIG): Forecasting the life of a healthy service