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Profiling, Prediction, and Capping of Power in Consolidated Environments Bhuvan Urgaonkar Computer Systems Laboratory The Penn State University Talk at Raritan, March 3, 2008
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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
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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
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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
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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
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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
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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
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8 Outline Motivation Power Profiles Power Prediction Preliminary Evaluation –Power Capping Ongoing Work
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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
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10 Offline Profiling Setup Signametrics SM2040 –Measurement rates: 0.2/sec - 1000/sec –Measurement range (AC): 2.5A –Interface: PCI
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11 Power Profile Derivation Power profile: Distribution derived from a representative run
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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)
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13 Power Profiles of Real Applications Applications –SPECjbb –Streaming –SPECInt Bzip, MCF –TPC-W t = I = 2 msec
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14 Variance of Power Profiles Higher variance (longer tails) for non CPU-saturating applications Bzip2Streaming
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15 Impact of DVFS State Non CPU-saturating apps at lower power states –CPU utilization increases –Power profile less bursty TPC-W, 60 clients
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16 Impact of DVFS State Power/performance trade- offs depend significantly on how CPU-saturating the application is TPC-W, 60 clients
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17 Outline Motivation Power Profiles Power Prediction Preliminary Evaluation –Power Capping Ongoing Work
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18 Average Power Upon Consolidation Consolidation of CPU saturating applications –Average of individual power consumptions
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19 Average Power Upon Consolidation Consolidation of CPU-saturating and non CPU-saturating –More complex: Some kind of additive effect
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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
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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
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22 Improved Estimate of Active Power Capturing non-idle power portion for TPC-W, 60 clients –CPU utilization was 40%
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23 Average Power Prediction: Some Results Prediction accuracy of 2% ! –Disclaimer: Pretty small degrees of consolidation
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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
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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!
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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
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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
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28 Example: Power-aware Application Packing Prediction techniques allow systematic answers to previous questions
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29 Outline Motivation Power Profiles Power Prediction Preliminary Evaluation Ongoing Work
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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
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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
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32 Thank You! Questions or comments? More information: –http://csl.cse.psu.edu
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