Adel Noureddine
Romain Rouvoy
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Abstract
Reducing the energy footprint of digital devices and software is a task challenging the research in Green IT. Researches have proposed approaches for energy management, ranging from reducing usage of software and hardware, compilators optimization, to server consolidation and software migration. However, optimizing the energy consumption requires knowledge of that said consumption. In particular, measuring the energy consumption of hardware and software is an important requirement for efficient energy strategies. In this review, we outline the different categories of approaches in energy measurements, and provide insights into example of each category. We draw recommendations from our review on requirements on how to efficiently measure energy consumption of devices and software.
- ANTS Performance profiler. http://www.redgate.com/products/dotnet-development/ants-performanceprofiler/.Google Scholar
- AQtime Pro. http://smartbear.com/products/developmenttools/performance-profiling/.Google Scholar
- C Profiler. http://www.semdesigns.com/Products/Profilers/CProfiler.html.Google Scholar
- GNU gprof. http://www.gnu.org/software/binutils/.Google Scholar
- Intel Energy Checker SDK. http://software.intel.com/enus/articles/intel-energy-checker-sdk.Google Scholar
- IPMItool. http://ipmitool.sourceforge.net/.Google Scholar
- Joulemeter. http://research.microsoft.com/enus/projects/joulemeter/.Google Scholar
- Openmoko Neo Freerunner. http://wiki.openmoko.org/wiki/Neo_FreeRunner.Google Scholar
- PowerTop. https://01.org/powertop/.Google Scholar
- SlimTune. http://code.google.com/p/slimtune/.Google Scholar
- The Java Interactive Profiler. http://jiprof.sourceforge.net.Google Scholar
- Watts Up Prp. http://www.wattsupmeters.com.Google Scholar
- AlertMe. http://www.alertme.com/smart_energy.Google Scholar
- A. Bourdon, A. Noureddine, R. Rouvoy, and L. Seinturier. Powerapi: A software library to monitor the energy consumed at the process-level. In PoweERCIM News, No. 92, January 2013.Google Scholar
- A. Carroll and G. Heiser. An analysis of power consumption in a smartphone. In Proceedings of the 2010 USENIX conference on USENIX annual technical conference, USENIXATC '10, pages 21--21, Berkeley, CA, USA, 2010. USENIX Association. Google ScholarDigital Library
- T. Do, S. Rawshdeh, and W. Shi. pTop: A Process-level Power Profiling Tool. In HotPower'09: Proceedings of the 2nd Workshop on Power Aware Computing and Systems, Big Sky, MT, USA, october 2009.Google Scholar
- ej-techonologies. JProfiler. http://www.ejtechnologies.com/products/jprofiler/overview.html.Google Scholar
- L. M. Feeney and M. Nilsson. Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In In IEEE Infocom, pages 1548--1557, 2001.Google ScholarCross Ref
- J. Flinn and M. Satyanarayanan. PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications. In Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications (WMCSA '99), page 2,Washington, DC, USA, 1999. IEEE Computer Society. Google ScholarDigital Library
- V. Gite. How do I Find Out Linux CPU Utilization? http://www.cyberciti.biz/tips/how-do-i-find-out-linuxcpu-utilization.html.Google Scholar
- Google Powermeter. http://www.google.com/powermeter.Google Scholar
- A. Kansal and F. Zhao. Fine-grained energy profiling for power-aware application design. In Proceedings of the 1st Workshop on Hot Topics in Measurement and Modeling of Computer Systems at ACMSigmetrics (HotMetrics'08), pages 26--31, june 2008.Google ScholarDigital Library
- K. Kant. Toward a science of power management. Computer, 42(9):99--101, september 2009. Google ScholarDigital Library
- C. F. Kelsey and V.M. GonzAa?lez. Understanding the use and adoption of home energy meters. In Extended Proceedings of El Congreso Latinoamericano de la Interaccişn Humano-Computadora, CLIHC'09, CLIHC'09, pages 64--71, 2009.Google Scholar
- A. Lewis, S. Ghosh, and N.-F. Tzeng. Run-time energy consumption estimation based on workload in server systems. In Proceedings of the 2008 conference on Power aware computing and systems, HotPower'08, pages 4--4, Berkeley, CA, USA, 2008. USENIX Association. Google ScholarDigital Library
- S. Liang and D. Viswanathan. Comprehensive profiling support in the javatm virtual machine. In Proceedings of the 5th conference on USENIX Conference on Object-Oriented Technologies & Systems - Volume 5, pages 17--17, Berkeley, CA, USA, 1999. USENIX Association. Google ScholarDigital Library
- Linux User's Manual. iostat. http://linux.die.net/man/1/iostat.Google Scholar
- Linux User's Manual. top. http://linux.die.net/man/1/top.Google Scholar
- Y.-H. Lu, Q. Qiu, A. R. Butt, and K.W. Cameron. End-to-end energy management. Computer, 44:75--77, 2011. Google ScholarDigital Library
- D. McIntire, T. Stathopoulos, and W. Kaiser. ETOP: sensor network application energy profiling on the LEAP2 platform. In Proceedings of the 6th international conference on Information processing in sensor networks (IPSN'07), pages 576--577, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- A. Noureddine, A. Bourdon, R. Rouvoy, and L. Seinturier. Runtime monitoring of software energy hotspots. In Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012, pages 160--169, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- A. Noureddine, A. Bourdon, R. Rouvoy, and L. Seinturier. A preliminary study of the impact of software engineering on greenit. In Green and Sustainable Software (GREENS), 2012 First International Workshop on, pages 21--27, June.Google Scholar
- OKTECH-Info Kft. OKTECH Profiler. http://code.google.com/p/oktech-profiler.Google Scholar
- L. Petre. Energy-Aware Middleware. In Proceedings of the 15th Annual International Conference and Workshop on the Engineering of Computer Based Systems (ECBS'08), pages 326--334. IEEE, 2008. Google ScholarDigital Library
- L. Petre, K. Sere, and M. Walden. A topological approach to distributed computing. Electronic Notes in Theoretical Computer Science, 28:59--80, 2000. WDS'99, Workshop on Distributed Systems (A satellite workshop to FCT'99).Google ScholarCross Ref
- L. Petre, K. Sere, and M. Walden. A language for modeling network availability. In Z. Liu and J. He, editors, Formal Methods and Software Engineering, volume 4260 of Lecture Notes in Computer Science, pages 639--659. Springer Berlin, Heidelberg, 2006. Google ScholarDigital Library
- J. Pouwelse, K. Langendoen, and H. Sips. Dynamic voltage scaling on a low-power microprocessor. In MMSA'00: Proceesings of the 2nd International Symposium on Mobile Multimedia Systems and Applications, pages 157--164, Delft, The Netherlands, 2000.Google Scholar
- O. Profiler. Sampling VS Instrumentation. http://code.google.com/p/oktechprofiler/wiki/SamplingVsInstrumentation.Google Scholar
- J. Reich, M. Goraczko, A. Kansal, and J. Padhye. Sleepless in seattle no longer. In Proceedings of the 2010 USENIX conference on USENIX annual technical conference, USENIXATC' 10, pages 17--17, Berkeley, CA, USA, 2010. USENIX Association. Google ScholarDigital Library
- C. Seo, S. Malek, and N. Medvidovic. An energy consumption framework for distributed java-based systems. In Proceedings of the 22nd IEEE/ACM international conference on Automated software engineering (ASE'07), pages 421--424, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- C. Seo, S. Malek, and N. Medvidovic. Estimating the energy consumption in pervasive java-based systems. In Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications, pages 243--247, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- The Linux Kernel. cpufrequtils. http://www.kernel.org/pub/linux/utils/kernel/ cpufreq/cpufrequtils.html.Google Scholar
- A. E. Trefethen and J. Thiyagalingam. Energy-aware software: Challenges, opportunities and strategies. Journal of Computational Science, (0), 2013.Google ScholarCross Ref
- VisualVM. http://visualvm.java.net.Google Scholar
- R. Xu, Z. Li, C. Wang, and P. Ni. Impact of data compression on energy consumption of wireless-networked handheld devices. In Proceedings of the 23rd International Conference on Distributed Computing Systems, ICDCS '03, pages 302--, Washington, DC, USA, 2003. IEEE Computer Society Google ScholarDigital Library
Index Terms
- A review of energy measurement approaches
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