profiling, prediction, and capping of power in consolidated environments
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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. Data Center Growth. Explosive growth in both size and numbers Serious implications on Robustness of operation - PowerPoint PPT PresentationTRANSCRIPT
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Profiling, Prediction, and Capping of Power in
Consolidated Environments
Bhuvan UrgaonkarComputer Systems Laboratory
The Penn State University
Talk at Raritan, March 3, 2008
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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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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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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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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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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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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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Outline
Motivation Power Profiles Power Prediction Preliminary Evaluation
– Power Capping
Ongoing Work
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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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Offline Profiling Setup
Signametrics SM2040– Measurement rates: 0.2/sec - 1000/sec– Measurement range (AC): 2.5A– Interface: PCI
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Power Profile Derivation
Power profile: Distribution derived from a representative run
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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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Power Profiles of Real Applications
Applications– SPECjbb– Streaming– SPECInt
• Bzip, MCF
– TPC-W
t = I = 2 msec
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Variance of Power Profiles
Higher variance (longer tails) for non CPU-saturating applications
Bzip2 Streaming
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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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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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Outline
Motivation Power Profiles Power Prediction Preliminary Evaluation
– Power Capping
Ongoing Work
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Average Power Upon Consolidation
Consolidation of CPU saturating applications– Average of individual power consumptions
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Average Power Upon Consolidation
Consolidation of CPU-saturating and non CPU-saturating– More complex: Some kind of additive effect
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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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A Simple Predictor for Average Power
Pidle: Power when no application/VM running– Note difference from leakage power
Pbusy/cpu and Pi/o for an application– CPU Power when application running– I/O power due to the application
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Improved Estimate of Active Power
Capturing non-idle power portion for TPC-W, 60 clients– CPU utilization was 40%
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Average Power Prediction: Some Results
Prediction accuracy of 2% !– Disclaimer: Pretty small degrees of consolidation
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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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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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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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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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Example: Power-aware Application Packing
Prediction techniques allow systematic answers to previous questions
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Outline
Motivation Power Profiles Power Prediction Preliminary Evaluation Ongoing Work
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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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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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Thank You!
Questions or comments?
More information:– http://csl.cse.psu.edu