predicting energy consumption by extending the palladio ...€¦ · fortiss gmbh an-institut...
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fortiss GmbH
An-Institut Technische Universität München
Stuttgart, Germany, 2014-11-27
Predicting Energy Consumption by Extending the
Palladio Component Model Symposium on Software Performance (SOSP) 2014
Felix Willnecker1, Andreas Brunnert1, Helmut Krcmar2
1fortiss GmbH, 2Technische Universität München
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pmw.fortiss.org SOSP 2014, Stuttgart, Germany, 2014-11-27 2
• Motivation
• Power Consumption Model
– Calculation
– Meta-Model Extension
– Power Consumption Model Generation
• Evaluation
– SPECjEnterprise 2010
– Runtastic for Android
• Conclusion
Agenda
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• Motivation
• Power Consumption Model
– Calculation
– Meta-Model Extension
– Power Consumption Model Generation
• Evaluation
– SPECjEnterprise 2010
– Runtastic for Android
• Conclusion
Agenda
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• Energy Consumption of Information and
Communication Technology is growing (Stobbe et al.
2009, Willnecker et al. 2014)
• Optimization on hardware and operating system
level cannot compensate rising demand (Gottschalk
et al. 2012)
• Investigating the energy efficiency on application
level becomes a growing software engineering
challenge (Brunnert et al. 2014)
• Main challenges and goals:
– Reduce operation costs in data centers
– Increase battery life time of portable devices
– Ease carbon footprint
Motivation
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• Performance metrics and energy consumption rely on the same underlying
parameters of resource demand and hardware capabilities
• Performance simulation and prediction techniques can be used to predict
the energy consumption of applications
Motivation
Performance
Model Simulation
Hardware
Workload
Software
Response time
Throughput
Resource demand
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• Performance metrics and energy consumption rely on the same underlying
parameters of resource demand and hardware capabilities
• Performance simulation and prediction techniques can be used to predict
the energy consumption of applications
Motivation
Performance
Model Simulation
Energy
consumption
Hardware
Workload
Software
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• Motivation
• Power Consumption Model
– Calculation
– Meta-Model Extension
– Power Consumption Model Generation
• Evaluation
– SPECjEnterprise 2010
– Runtastic for Android
• Conclusion
Agenda
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Calculation
• This function is calculated for each resource container or linking resource
based on the utilization of the components in this resource container.
• Ppred is the Predicted Power Consumption of a resource container
• Pidle,0 is the Power Consumption function of the resource container in idle
state
• Pidle,i is the Power Consumption factor function of a component in this
resource container
• ui is the utilization factor of the component (e.g, CPU utilization, GPS on/off
state, throughput)
Power Consumption Model
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Meta-Model Extension
Power Consumption Model
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Meta-Model Extension
Power Consumption Model
Resource Container
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Meta-Model Extension
Power Consumption Model
Linking Resource
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Power Consumption Model of a server
Power Consumption Model
• Ppred = 200 + 300 x uCPU + 50 x uHDD
• The energy consumption E of the system is the
integral over Ppred over the simulation time T
Server in PCM with Power Consumption Model (Brunnert et al. 2014b)
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Power Consumption Model of a mobile device
Power Consumption Model
• Battery capacity specified
to calculate discharging
• Stochastic functions for
varying power
consumptions
• Added two resource types:
GPS and DISPLAY
• Linking resource power
consumption model to
calculate energy demand of
network traffic based on
throughput Mobile Device in PCM with Power Consumption Model
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Power Consumption Model Generation
• Manually creation based on specifications and
estimations
– Resource Specifications from manufacturer
– Android Vendor Profiles
• Calibration by stressing resources independently
– Intelligent Plattform Management Interface
– Android Calibration App
Power Consumption Model
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• Motivation
• Power Consumption Model
– Calculation
– Meta-Model Extension
– Power Consumption Model Generation
• Evaluation
– SPECjEnterprise 2010
– Runtastic for Android
• Conclusion
Agenda
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SPECjEnterprise 2010
Evaluation
System Under Test (Brunnert et al. 2014b)
IBM System X3755M3
Virtual Server (VM Ware ESXi 5.0.0)
openSuse 12.3
Benchmark
Driver
Load
Balancer
Load Driver
AMD-based Server
Intel-based Server
IBM System X3550M3
openSuse 12.3
DB
JBoss
Application
Server
JBoss
Application
Server
IBM System X3755M3
openSuse 12.2
DB
JBoss
Application
Server
JBoss
Application
Server
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SPECjEnterprise 2010
Evaluation
Clients MMPC1 SMPC2 PCPE3
1300 367.55 W 320.26 W 12.87 %
2300 403.87 W 352.22 W 12.79 %
3300 433.76 W 384.52 W 11.35 %
3500 436.47 W 390.95 W 10.43 %
Clients MMPC1 SMPC2 PCPE3
1300 197.05 W 175.94 W 10.71 %
2300 220.47 W 194.93 W 11.58 %
3300 241.67 W 213.91 W 11.49 %
4300 264.29 W 232.69 W 11.96 %
1 Measured Mean Power Consumption
2 Simulated Mean Power Consumption
3 Power Consumption Prediction Error
Measured and simulated results for the AMD-based server (Brunnert et al. 2014b)
Measured and simulated results for the Intel-based server (Brunnert et al. 2014b)
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Runtastic for Android
Evaluation
• Nexus 5
running Android 4.4
• Galaxy Tab
running Android 4.3
• Runtastic
running on both devices
30 mins run
Nexus 5 Galaxy
Tab
MMPC1 0.883 W 1.251 W
SMPC2 0.732 W 1.084 W
PCPE3 17.12 % 13.35 %
BLPE4 0.67 % 1.01 %
1 Measured Mean Power Consumption
2 Simulated Mean Power Consumption
3 Power Consumption Prediction Error
4 Battery Level Prediction Error
Measured and simulated results for
the mobile devices (Leimhofer 2014)
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• Motivation
• Power Consumption Model
– Calculation
– Meta-Model Extension
– Power Consumption Model Generation
• Evaluation
– SPECjEnterprise 2010
– Runtastic for Android
• Conclusion
Agenda
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• Energy consumption can be predicted using the Palladio Component Model
with an error of mostly below 13% for server systems and 17,2 % for mobile
devices.
• Multi-Processor Environments for mobile hard to calibrate
• Extension for other device types (Windows, iOS, etc.)
• Additional resource types (e.g., accelerometer) are necessary for mobile
device evaluations
• Power Consumption Model Repository for different devices from different
vendors.
• Automatic Performance Model Generation for mobile devices.
Conclusion
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Brunnert, A.; Vögele, C.; Danciu, A.; Pfaff, M.; Mayer, M.; Krcmar, H. (2014): Performance Management Work. In:
Business & Information Systems Engineering, Vol. 6 (2014), pp. 1-3.
Brunnert, A.; Wischer, K.; Krcmar, H. (2014): Using Architecture-Level Performance Models as Resource Profiles
for Enterprise Applications. In: Proceedings of the 10th ACM SIGSOFT International Conference on the Quality of
Software Architectures (QoSA), Lille, France.
Gottschalk, M.; Josefiok, M.; Jelschen, J.; Winter, A. (2012): Removing Energy Code Smells with Reengineering
Services. Paper presented at the GI-Jahrestagung, Braunschweig, Germany, pp. 441-455.
Leimhofer, J. (2014): Predicting the Energy Consumption of Mobile Applications using Simulations. Bachelor's
Thesis, Technische Universität München 2014.
Stobbe, L.; Nissen, N.; Proske, M.; Middendorf, A.; Schlomann, B.; Friedewald, M.; Georgieff, P.; Leimbach, T.
(2009): Abschätzung des Energiebedarfs der weiteren Entwicklung der Informationsgesellschaft. In: Abschlussbericht
an das Bundesministerium für Wirtschaft und Technologie. Berlin, Karlsruhe: Fraunhofer IZM, (2009).
Willnecker, F.; Brunnert, A.; Krcmar, H. (2014): Model-based Energy Consumption Prediction for Mobile
Application. In: Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental
Protection, Sustainable Development and Risk Management, (2014).
Bibliography
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Q&A
Felix Willnecker,
Andreas Brunnert [email protected]
pmw.fortiss.org