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Modular Energy Modeling using Energy/Utility

Published:02 April 2018Publication History

ABSTRACT

The modeling of the relationship between power usage and performance for complex computing systems is challenging due to the vast amount of tunable parameters that influence both metrics. To simplify the energy management of information systems from individual embedded machines to whole data centers we use a modular, hierarchical concept called Energy/Utility to model individual parts of a system. We present first results that show the decomposition of an individual asymmetric multi-processing system into hardware and software models. We show that using the Energy/Utility approach these models can stay manageable reducing total benchmark running time and modeling overhead while providing sufficiently high precision for performance and energy usage prediction.

References

  1. Frank Bellosa. 2000. The benefits of event: driven energy accounting in power-sensitive systems. In Proceedings of the 9th workshop on ACM SIGOPS European workshop: beyond the PC: new challenges for the operating system. ACM, 37-42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ramon Bertran, Yolanda Becerra, David Carrera, Vicenç Beltran, Marc Gonzalez, Xavier Martorell, Jordi Torres, and Eduard Ayguade. 2010. Accurate energy accounting for shared virtualized environments using pmc-based power modeling techniques. In Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on. IEEE, 1-8.Google ScholarGoogle ScholarCross RefCross Ref
  3. Mario Bielert, Florina Ciorba, Kim Feldhoff, Thomas Ilsche, and Wolfgang Nagel. 2015. HAEC-SIM: A Simulation Framework for Highly Adaptive Energy-Efficient Computing Platforms. EAI Endorsed Transactions on Energy Web 16, 8 (8 2015).Google ScholarGoogle Scholar
  4. William Lloyd Bircher and Lizy K John. 2012. Complete system power estimation using processor performance events. IEEE Trans. Comput. 61, 4 (2012), 563-577. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ata E Husain Bohra and Vipin Chaudhary. 2010. VMeter: Power modelling for virtualized clouds. In Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on. Ieee, 1-8.Google ScholarGoogle Scholar
  6. Aaron Carroll and Gernot Heiser. 2010. An analysis of power consumption in a smartphone. (2010).Google ScholarGoogle Scholar
  7. Maxime Colmant, Mascha Kurpicz, Pascal Felber, Loïc Huertas, Romain Rouvoy, and Anita Sobe. 2015. Process-level power estimation in vm-based systems. In Proceedings of the Tenth European Conference on Computer Systems. ACM, 14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Thanh Do, Suhib Rawshdeh, and Weisong Shi. 2009. ptop: A process-level power profiling tool. (2009).Google ScholarGoogle Scholar
  9. Dimitris Economou, Suzanne Rivoire, Christos Kozyrakis, and Partha Ranganathan. 2006. Full-system power analysis and modeling for server environments. International Symposium on Computer Architecture-IEEE.Google ScholarGoogle Scholar
  10. Marcus Hähnel, Björn Döbel, Marcus Völp, and Hermann Härtig. 2012. Measuring energy consumption for short code paths using RAPL. ACM SIGMETRICS Performance Evaluation Review 40, 3 (2012), 13-17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Marcus Hähnel, Björn Döbel, Marcus Völp, and Hermann Härtig. 2013. eBond: energy saving in heterogeneous RAIN. In Proceedings of the fourth international conference on Future energy systems. ACM, 193-202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Marcus Hähnel and Hermann Härtig. 2014. Heterogeneity by the Numbers: A Study of the ODROID XU+E big.LITTLE Platform. In 6th Workshop on Power-Aware Computing and Systems (HotPower 14). USENIX Association, Broomfield, CO. https://www.usenix.org/conference/hotpower14/workshop-program/presentation/hahnel Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hermann Hartig, Marcus Volp, and Marcus Hahnel. 2013. The case for practical multi-resource and multi-level scheduling based on energy/utility. In Embedded and Real-Time Computing Systems and Applications (RTCSA), 2013 IEEE 19th International Conference on. IEEE, 175-182.Google ScholarGoogle ScholarCross RefCross Ref
  14. Timo Hönig, Heiko Janker, Christopher Eibel, Oliver Mihelic, and Rüdiger Kapitza. 2014. Proactive Energy-Aware Programming with PEEK. In 2014 Conference on Timely Results in Operating Systems (TRIOS 14). USENIX Association, Broomfield, CO. https://www.usenix.org/conference/trios14/technical-sessions/presentation/hoenig Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Canturk Isci and Margaret Martonosi. 2003. Runtime power monitoring in high-end processors: Methodology and empirical data. In Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, 93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Victor Jimenez, Francisco Cazorla, Roberto Gioiosa, Eren Kursun, Canturk Isci, Alper Buyuktosunoglu, Pradip Bose, and Mateo Valero. 2011. Energy-aware accounting and billing in large-scale computing facilities. IEEE Micro 31, 3 (2011), 60-71. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Russ Joseph and Margaret Martonosi. 2001. Run-time power estimation in high performance microprocessors. In Proceedings of the 2001 international symposium on Low power electronics and design. ACM, 135-140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Vasilios Konstantakos, Alexander Chatzigeorgiou, Spiridon Nikolaidis, and Theodore Laopoulos. 2008. Energy consumption estimation in embedded systems. IEEE Transactions on instrumentation and measurement 57, 4 (2008), 797-804.Google ScholarGoogle ScholarCross RefCross Ref
  19. Tao Li and Lizy Kurian John. 2003. Run-time modeling and estimation of operating system power consumption. In ACM SIGMETRICS Performance Evaluation Review, Vol. 31. ACM, 160-171. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Yepang Liu, Chang Xu, and Shing-Chi Cheung. 2013. Where has my battery gone? Finding sensor related energy black holes in smartphone applications. In Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on. IEEE, 2-10.Google ScholarGoogle Scholar
  21. Tuomo Malkamäki and Seppo J Ovaska. 2016. Modeling power flow in computer and server systems. In Proceedings of the 2nd International Workshop on Energy- Aware Simulation. ACM, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Heike McCraw, James Ralph, Anthony Danalis, and Jack Dongarra. 2014. Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models. In Cluster Computing (CLUSTER), 2014 IEEE International Conference on. IEEE, 385-391.Google ScholarGoogle ScholarCross RefCross Ref
  23. Abhinav Pathak, Y Charlie Hu, and Ming Zhang. 2012. Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof. In Proceedings of the 7th ACM european conference on Computer Systems. ACM, 29-42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Abhinav Pathak, Y Charlie Hu, Ming Zhang, Paramvir Bahl, and Yi-Min Wang. 2011. Fine-grained power modeling for smartphones using system call tracing. In Proceedings of the sixth conference on Computer systems. ACM, 153-168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kai Shen, Arrvindh Shriraman, Sandhya Dwarkadas, Xiao Zhang, and Zhuan Chen. 2013. Power containers: An OS facility for fine-grained power and energy management on multicore servers. In ACM SIGPLAN Notices, Vol. 48. ACM, 65-76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Alex Shye, Benjamin Scholbrock, and Gokhan Memik. 2009. Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In Microarchitecture, 2009. MICRO-42. 42nd Annual IEEE/ACM International Symposium on. IEEE, 168-178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Tajana Simunic, Luca Benini, and Giovanni De Micheli. 1999. Cycle-accurate simulation of energy consumption in embedded systems. In Design Automation Conference, 1999. Proceedings. 36th. IEEE, 867-872. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T Smejkal, M Hähnel, T Ilsche, M Roitzsch, WE Nagel, and H Härtig. 2017. E-Team: Practical Energy Accounting for Multi-Core Systems. In 2017 USENIX Annual Technical Conference (USENIX ATC 17). USENIX Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. David C Snowdon, Etienne Le Sueur, Stefan M Petters, and Gernot Heiser. 2009. Koala: A platform for OS-level power management. In Proceedings of the 4th ACM European conference on Computer systems. ACM, 289-302. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. David C Snowdon, Stefan M Petters, and Gernot Heiser. 2007. Accurate online prediction of processor and memoryenergy usage under voltage scaling. In Proceedings of the 7th ACM & IEEE international conference on Embedded software. ACM, 84-93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jan Treibig, Georg Hager, and Gerhard Wellein. 2010. Likwid: A lightweight performance-oriented tool suite for x86 multicore environments. In Parallel Processing Workshops (ICPPW), 2010 39th International Conference on. IEEE, 207-216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Narayanan Vijaykrishnan, Mahmut Kandemir, Mary Jane Irwin, Hyun Suk Kim, and Wu Ye. 2000. Energy-driven integrated hardware-software optimizations using SimplePower. ACM SIGARCH Computer Architecture News 28, 2 (2000), 95-106. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Heng Zeng, Carla S Ellis, Alvin R Lebeck, and Amin Vahdat. 2002. ECOSystem: Managing energy as a first class operating system resource. In ACM Sigplan Notices, Vol. 37. ACM, 123-132. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        ICPE '18: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering
        April 2018
        212 pages
        ISBN:9781450356299
        DOI:10.1145/3185768

        Copyright © 2018 ACM

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        Publication History

        • Published: 2 April 2018

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