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Power-efficient server provisioning in server farms

Anshul Gandhi (Carnegie Mellon University) Varun Gupta (CMU), Mor Harchol-Balter (CMU) Michael Kozuch (Intel, Pittsburgh). Power-efficient server provisioning in server farms. Motivation. Server farms are important for today’s IT infrastructure (Amazon, Google, IBM, HP, …)

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Power-efficient server provisioning in server farms

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  1. Anshul Gandhi (Carnegie Mellon University) Varun Gupta (CMU), Mor Harchol-Balter (CMU) Michael Kozuch (Intel, Pittsburgh) Power-efficient server provisioning in server farms

  2. Motivation • Server farms are important for today’s IT infrastructure (Amazon, Google, IBM, HP, …) • However, server farms cost a lot of money to power ($4 billion in 2006) Server Farm Requests

  3. High-level problem statement • How many servers, given request rate ? • Don’t want to waste power Server Farm Requests

  4. Outline • Server farm model • Provisioning for fixed arrival rate • Provisioning for unpredictable, time-varying arrival rate • Future work

  5. Server farms IDLE servers consume a lot of power ~ 60 % of BUSY

  6. Server farms Turn IDLE servers OFF to save power HOWEVER

  7. Setup cost To turn on an OFF server .. • BUSY • OFF • SETUP • Time delay (setup time) • 1 min – 5 mins • and • Power penalty • peak power during setup time

  8. Setup cost To turn on an OFF server .. • BUSY • OFF • SETUP Should we ever turn servers OFF ?

  9. Server model • Server states: BUSY PBUSY 240 W IDLE PIDLE 150 W OFF POFF 0 W SETUP PSETUP 240 W • Setup times: TOFF→ON 200 s TON→OFF 0 s ON • Intel Xeon E5320 • 2 X 1.86 GHz quad-core • 4GB memory

  10. Server farm model • Poisson arrival process: λ(t) requests/sec • Exponentially distributed job sizes: E[S] secs • Load: ρ(t) = λ(t) ∙ E[S] Minimum # servers to handle incoming load Server Farm Requests FCFS

  11. Metric • Interested in response time and power conumption • Perf/W = 1/(Mean RT X Mean Power) • Maximize Perf/W

  12. Outline • Server farm model • Provisioning for fixed arrival rate • Provisioning for unpredictable, time-varying arrival rate • Future work

  13. Provisioning for fixed arrival rate • Existing solutions: prediction based, reactive controllers. • Is there a simple, yet, near-optimal solution ? Poisson arrivals Server Farm Max. Perf/W

  14. NEVEROFF • Keep n servers always ON (M/M/n) • Servers are BUSY or IDLE

  15. Perf/W for NEVEROFF

  16. INSTANTOFF • Turn servers OFF when IDLE • Servers are BUSY, OFF or in SETUP Auto-scales if n is high

  17. Perf/W for INSTANTOFF

  18. NEVEROFF vs. INSTANTOFF TON→OFF < γ E[S]/√ρ

  19. Near-optimality • Best of {NEVEROFF, INSTANTOFF} is optimal for single-server • Multi-server ? For ρ > 10, we are within 20% of OPT

  20. Outline • Server farm model • Provisioning for fixed arrival rate • Provisioning for unpredictable, time-varying arrival rate • Future work

  21. Unpredictable, time-varying demand • Data center demand has daily variations • INSTANTOFF can auto-scale

  22. Unpredictable, time-varying demand • NEVEROFF requires continual updates based on predicted load • Predictions are not always accurate • Can we find a simple traffic-oblivious policy? • Auto-scaling in nature

  23. DELAYEDOFF • Like INSTANTOFF, except we wait for twait seconds before turning IDLE servers OFF • Routing ? MRB routing is crucial !

  24. twait • Rule of thumb:twait ∙ PIDLE = TOFF→ON ∙ PON

  25. Near-optimality Worse at higher frequencies

  26. Auto-scaling capabilities • 1998 World Cup Soccer trace (ITA)

  27. Outline • Server farm model • Provisioning for fixed arrival rate • Provisioning for unpredictable, time-varying arrival rate • Future work

  28. Future work • Experimental evaluation of proposed schemes • Preliminary experiments on 15-server testbed using CPU-bound workload and sinusoidal arrival pattern • Experimental results agree with analysis • Web workloads: • What does the experimental setup look like ? • Try out various arrival traces and workloads

  29. Thank You! • Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael Kozuch Optimality analysis of energy-performance trade-off for server farm management, PERFORMANCE 2010 • Anshul Gandhi, Mor Harchol-Balter, Ivo Adan Server farms with setup costs, PERFORMANCE 2010 • Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael Kozuch Energy-efficient dynamic capacity provisioning in server farms, CMU technical report CMU-CS-10-108

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