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Optimal Power Allocation in Server Farms

Optimal Power Allocation in Server Farms. ANSHUL GANDHI Carnegie Mellon Univ. U.S. Data Center Energy Consumption. 120 billion kWh. 50 billion kWh. kWh (in billions) . $ 8.4 billion. 12 billion kWh. Source: EPA report to Congress on Server and Data Center Energy Efficiency ,2007. Goal.

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Optimal Power Allocation in Server Farms

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  1. Optimal Power Allocation in Server Farms ANSHUL GANDHI Carnegie Mellon Univ.

  2. U.S. Data Center Energy Consumption 120 billion kWh 50 billion kWh kWh (in billions)  $ 8.4 billion 12 billion kWh Source: EPA report to Congress on Server and Data Center Energy Efficiency ,2007

  3. Goal Get the best performance from the power, P, that we have. Data Center P

  4. GoalHow to split P to minimize mean response time? Right answer can improve performance by up to 5X P1 P P2 P3 Constraint: P ≥ P1 + P2 + P3

  5. Power Efficient Load Balancer Frequency = server speed Freq (GHz) Freq (GHz) Output Power (Watts) Power (Watts) q1 Input P Speed scaling Workload Arrival rate Open vs. Closed Max speed Min speed . . P1 P q2 POWER EFFICIENT LOAD BALANCER P2 P3 q3

  6. Outline Experimental Setup Power  Speed How power affects server speed for a single server Speed  Response time How response time of server farm depends on individual server speeds Optimal power allocation Theorems and Experiments

  7. Experimental Setup • Blade • Intel Xeon 5000 series • 3 GHz, quad core • 4 GB RAM • Scaling tech. • DFS, DVFS, DVFS+DFS • Workload • CPU bound (LINPACK, DAXPY) • Memory bound (STREAM) • Other (WebBench, GZIP, BZIP2) IBM BladeCenter HS21 Rack with 7 blade servers P1 P POWER EFFICIENT LOAD BALANCER P2 P3

  8. Outline Experimental Setup Power  Speed How power affects server speed for a single server Speed  Response time How response time of server farm depends on individual server speeds Optimal power allocation Theorems and Experiments

  9. Our Experimental Results How power affects server speed for a single server DFS: Dynamic Frequency Scaling Frequency (GHz) (server speed) DFS “linear” P = system power NOT processor power Power (Watts)

  10. Our Experimental Results How power affects server speed for a single server DVFS DVFS +DFS “LINPACK” CPU BOUND Frequency (GHz) DFS Frequency (GHz) Frequency (GHz) Power (Watts) Power (Watts) Power (Watts) DVFS DVFS +DFS DFS “STREAM” MEM BOUND Frequency (GHz) Frequency (GHz) Frequency (GHz) Power (Watts) Power (Watts) Power (Watts)

  11. Outline Experimental Setup Power  Speed How power affects server speed for a single server Speed  Response time How response time of server farm depends on individual server speeds Optimal power allocation Theorems and Experiments

  12. Pop Quiz High arrival rate • Given P = 720W and DVFS. • Which allocation is better? • 180|180|180|180 • 240| 240|240|0 180 x 4 DVFS Results PowMin Response Time (sec) PowMax 240 x 3 • 2. Given P = 720W and DFS. • Which allocation is better? • 180|180|180|180 • 240| 240|240|0 PowMin PowMax 240 x 3 DFS Results 180 x 4 Response Time (sec)

  13. Pop Quiz Low arrival rate • Given P = 720W and DVFS. • Which allocation is better? • 180|180|180|180 • 240| 240|240|0 DVFS Results PowMin Response Time (sec) 180 x 4 PowMax 240 x 3 • 2. Given P = 720W and DFS. • Which allocation is better? • 180|180|180|180 • 240| 240|240|0 PowMin PowMax DFS Results 180 x 4 Response Time (sec) 240 x 3

  14. Abstract Model of Server Farm Each server: Processor Sharing q1 s1 P1 P q2 POWER EFFICIENT LOAD BALANCER s2 P2 Poisson arrivals With rate λ jobs/sec s3 P3 q3

  15. Response Time for Server Farm (Mean Resp. Time) • Non-linear in si and qi • If λ:low • If λ:high PowMin PowMin results in poor utilization of some servers All server well utilized. Choice of PowMin vs. PowMax depends on scaling tech. PowMin PowMax

  16. Outline Experimental Setup Power  Speed How power affects server speed for a single server Speed  Response time How response time of server farm depends on individual server speeds Optimal power allocation Theorems and Experiments

  17. Power Allocation Choices PowMin DVFS Ex: P = 720W PowMin = 4 X 180 DFS DVFS +DFS Frequency (GHz) PowMax Ex: P = 720W PowMax = 3 X 240 180 210 240 PowMed Ex: P = 720W PowMed = 3 X 210

  18. Power Allocation Theorems OUTPUT Optimal Power Allocation INPUTS System Parameters linear steep linear flat cubic PowMin • Speed scaling technology • Workload type • Pmin, Pmax • Arrival rate: (2 regimes) • Open vs. Closed workload configuration PowMax THEOREMS λ < λ0 λ ≥λ0 PowMed

  19. Power Allocation Results: Outline

  20. Power Allocation Results DFS Frequency (GHz) Power (Watts)

  21. Power Allocation Results DVFS Frequency (GHz) Power (Watts)

  22. Power Allocation Results DVFS +DFS Frequency (GHz) Power (Watts)

  23. Power Allocation Results DFS Mean Resp. Time (sec) Arrival rate (jobs/sec) DVFS DVFS+DFS Mean Resp. Time (sec) Mean Resp. Time (sec) Arrival rate (jobs/sec) Arrival rate (jobs/sec)

  24. Conclusions: How to allocate power optimally Speed Scaling? Linear, Steep Linear, Flat Cubic Arrival Rate? Arrival Rate? Arrival Rate? High Low Low High High Low PowMax PowMax PowMax PowMin PowMax PowMed

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