Days In Green (DIG): Forecasting the life of a healthy service
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This document describes Twitter's Days In Green (DIG) methodology for forecasting the lifespan of a healthy service before it exceeds a predefined capacity threshold. It involves collecting time series data on a service's key performance metric, detecting anomalies and breakouts, fitting an ARIMA model to capture trends and seasonality, and forecasting the number of days before the threshold is breached to determine capacity needs. The methodology has been deployed at Twitter to help plan capacity for hundreds of services and detect those nearing disaster recovery thresholds.
![#WorldCup
[1]
hBp://www.newsweek.com/twiBer-‐us-‐soccer-‐what-‐radio-‐was-‐baseball-‐256336
(June
2014)
[1]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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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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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