1. Profiling, Prediction, and Capping of Power in Consolidated Environments Bhuvan Urgaonkar Computer Systems Laboratory The Penn State University Talk at Raritan, March 3, 2008

2. 2 Data Center Growth  Explosive growth in both size and numbers  Serious implications on –Robustness of operation Heterogeneity of hardware/software, workloads, … Potential lack of scalability of existing resource management solutions Flash crowds –Cost of operation Administrative costs Power consumption

3. 3 Data Center Growth  Explosive growth in both size and numbers  Serious implications on –Robustness of operation Heterogeneity of hardware/software, workloads, … Potential lack of scalability of existing resource management solutions Flash crowds –Cost of operation Administrative costs Power consumption

4. 4 Growing Power Consumption in Data Centers  Significant energy consumption and growing! –Up to 1.2% of overall power consumption within the US –Growing @ 40% every five years  Growing number of servers main culprit –Increase-per-unit less significant contributor  Lot of interest in dampening this growth  Key technique: Consolidation

5. 5 Consolidation in Data Centers  Goal: Operating/provisioning fewest possible hardware resources while meeting service-level agreements –Our focus: Servers  Multiple spatial scales –Packing multiple applications on a server –Reducing the number of data centers operated by a company  Key ingredients of existing consolidation solutions –Workload characterization and prediction Resource requirement inference –Dynamic resource provisioning Efficient statistical multiplexing

6. 6 Power Consumption in Consolidated Data Centers  How does consolidation affect power consumption? –How does the power consumed by consolidated aggregates relate to those of individuals? Spatial –Applications, servers, racks, … Temporal –Long-term averages: energy consumed, thermal profiles –Short-term peaks: fuses, circuit breakers –Can we effectively predict these phenomena? How should we characterize power consumption of individuals? –Such that we can meaningfully infer behavior upon consolidation  Benefits/utility of such characterization and prediction –Enable consolidation that adheres to “power budgets” –Adapt placement to changes in workloads to obtain desired performance/power behavior –Determine optimal “power states” (if any) exposed by hardware E.g., CPU DVFS states

7. 7 Average and Sustained Power Consumption  Two quantities of interest –Average power consumption –Sustained power consumption  Average and sustained power “budgets” or “caps” of interest at various spatial levels  Our focus: single server consolidating multiple applications

8. 8 Outline Motivation  Power Profiles  Power Prediction  Preliminary Evaluation –Power Capping  Ongoing Work

9. 9 Characterizing Power Consumption  Desirable features –Easy/efficient to realize –Amenable to meaningful statistical aggregation  Our approach: Based on offline profiling –Run application in isolation and subject it to realistic workload –Measure power consumption over intervals of chosen length and construct a PDF Power profile

10. 10 Offline Profiling Setup  Signametrics SM2040 –Measurement rates: 0.2/sec - 1000/sec –Measurement range (AC): 2.5A –Interface: PCI

11. 11 Power Profile Derivation  Power profile: Distribution derived from a representative run

12. 12 Characterizing Power Consumption  Desirable features –Easy/efficient to realize –Amenable to meaningful statistical aggregation  Our approach: Based on offline profiling –Run application in isolation and subject it to realistic workload –Measure power consumption over intervals of chosen length and construct a PDF Power profile  Other noteworthy points –Xen-based virtualized hosting Each application hosted within a Xen domain –Easy to do such profiling online –Also measure resource usage and provision enough resources when consolidating Appropriate resource managers within the Xen VMM –Dell PowerEdge server (DVFS-capable CPU)

13. 13 Power Profiles of Real Applications  Applications –SPECjbb –Streaming –SPECInt Bzip, MCF –TPC-W  t = I = 2 msec

14. 14 Variance of Power Profiles  Higher variance (longer tails) for non CPU-saturating applications Bzip2Streaming

15. 15 Impact of DVFS State  Non CPU-saturating apps at lower power states –CPU utilization increases –Power profile less bursty TPC-W, 60 clients

16. 16 Impact of DVFS State  Power/performance trade- offs depend significantly on how CPU-saturating the application is TPC-W, 60 clients

17. 17 Outline Motivation Power Profiles  Power Prediction  Preliminary Evaluation –Power Capping  Ongoing Work

18. 18 Average Power Upon Consolidation  Consolidation of CPU saturating applications –Average of individual power consumptions

19. 19 Average Power Upon Consolidation  Consolidation of CPU-saturating and non CPU-saturating –More complex: Some kind of additive effect

20. 20 Average Power: What’s Going On?  CPU+CPU: –Sole significant consumer of power is being time-shared  CPU+non-CPU: –CPU being time-shared Though not equally, since non-CPU apps block –CPU and I/O devices being used simultaneously  Insight #1: Separate out power due to resources (such as CPU and I/O)  Insight #2: Also consider the utilization of relevant resources

21. 21 A Simple Predictor for Average Power  P idle : Power when no application/VM running –Note difference from leakage power  P busy/cpu and P i/o for an application –CPU Power when application running –I/O power due to the application

22. 22 Improved Estimate of Active Power  Capturing non-idle power portion for TPC-W, 60 clients –CPU utilization was 40%

23. 23 Average Power Prediction: Some Results  Prediction accuracy of 2% ! –Disclaimer: Pretty small degrees of consolidation

24. 24 Sustained Power Prediction  Goal: Predict the probability that at least S units of power would be consumed for L consecutive seconds  What’s difficult? –Applications that individually do not violate a sustained budget can do so upon consolidation

25. 25 Sustained Power Prediction  What’s difficult? –Applications that individually do not violate a sustained budget can do so upon consolidation time power S L Violation!

26. 26 Sustained Power Prediction: Some Results  Harder to predict than average –More sophisticated statistical techniques –Omitting details, please find them in our technical report  Good news: Our profile-based techniques appear to do a good job

27. 27 Example: Power-aware Application Packing  Average budget 180 W  Sustained budget 185 W, 1 sec  Questions: How many apps can be consolidated and at what DVFS state? S1 S2

28. 28 Example: Power-aware Application Packing  Prediction techniques allow systematic answers to previous questions

29. 29 Outline Motivation Power Profiles Power Prediction Preliminary Evaluation  Ongoing Work

30. 30 Ongoing Work  Extending prediction and capping to the rack-level –Employing Raritan’s PDU model DPCR 20-20 –Use PDU measurements for online profiling –Prediction of power behavior based on these profiles  Incorporating the storage sub-system’s power consumption –SAN-based array connected to PDU Exploring similar profiling techniques –Flash-based storage

31. 31 Ongoing Work  Efficient provisioning of power infrastructure –Motivation: Recent studies, e.g., ISCA paper from Google –Use prediction techniques to enable closer-to-capacity operation –Overbook power? –Dynamic adjustment of power caps Trade-off energy consumption versus revenue and thermal constraints

32. 32 Thank You!  Questions or comments?  More information: –http://csl.cse.psu.edu