How Green Are Java Best Coding Practices?

Jérôme Rocheteau, Virginie Gaillard, Lamya Belhaj

Abstract

This paper aims at explaining both how to measure energy consumption of Java source codes and what kind of conclusions can be drawn of these measures. This paper provides a formalization of best coding practices with a semantics based on quantitative metrics that correspond to the time, memory and energy saved while applying best coding practices. This paper also explains how to measure such source codes in order to provide repeatable and stable measures by combining both physical and logical sensors.

References

  1. Bloch, J. (2008). Effective Java. The Java series. Pearson Education.
  2. Boudon, A. (2013). Power API: Library API to monitor energy spent at the process-level. http://abourdon.github.io/powerapi-akka.
  3. Douglas, D. H. and Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. 10(2):112-122.
  4. Fleiss, J. L. (1981). Statistical Methods for Rates and Proportions. Wiley, John and Sons, Incorporated, New York, N.Y.
  5. Garrett, M. (2007). Powering down. ACM Queue, 5(7):16- 21.
  6. Koskela, L. (2013). Effective Unit Testing: A Guide for Java Developers. Running Series. Manning Publications Company.
  7. Lafond, S. and Lilius, J. (2006). An energy consumption model for an embedded java virtual machine. In Architecture of Computing Systems - ARCS 2006, 19th International Conference, Frankfurt/Main, Germany, March 13-16, 2006, Proceedings, volume 3894 of Lecture Notes in Computer Science, pages 311-325. Springer.
  8. Noureddine, A., Bourdon, A., Rouvoy, R., and Seinturier, L. (2012a). 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. IEEE.
  9. Noureddine, A., Bourdon, A., Rouvoy, R., and Seinturier, L. (2012b). Runtime monitoring of software energy hotspots. In Automated Software Engineering (ASE), 2012 Proceedings of the 27th IEEE/ACM International Conference on, pages 160-169. IEEE.
  10. Poess, M. and Nambiar, R. O. (2008). Energy cost, the key challenge of today's data centers: a power consumption analysis of tpc-c results. Proceedings of the Very Large Ddata Bases endowment, 1(2):1229-1240.
  11. Saxe, E. (2010). Power-efficient software. ACM Queue, 8(1):10.
  12. Seo, C., Malek, S., and Medvidovic, N. (2007). An energy consumption framework for distributed javabased systems. In 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007), November 5-9, 2007, Atlanta, Georgia, USA, pages 421-424. ACM.
  13. Sestoft, P. (2005). Java Precisely. MIT Press.
  14. Tsirogiannis, D., Harizopoulos, S., and Shah, M. A. (2010). Analyzing the energy efficiency of a database server. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6-10, 2010, pages 231-242. ACM.
  15. van de Ven, A. and Accardi, K. (2013). PowerTOP: a Linux tool to diagnose issues with power consumption and power management. https://01.org/powertop.
  16. Webb, M. (2008). Smart 2020 enabling the low carbon economy in the information age.
  17. Wilke, C., Gtz, S., Cech, S., Waltsgott, J., and Fritzsche, R. (2011a). Aspects of softwares energy consumption. Technical Report TUD-FI11-04, ISSN 1430- 211X, Technische Universitt Dresden.
  18. Wilke, C., Gtz, S., Reimann, J., and Assmann, U. (2011b). Vision paper: Towards model-based energy testing. In Proceedings of 14th International Conference on Model Driven Engineering Languages and Systems (MODELS 2011).
  19. Wilke, C., Gtz, S., and Richly, S. (2013). JouleUnit A Generic Framework for Software Energy Profiling and Testing. In Software Engineering Green By Software Engineering Workshop. lhttp://code.google.com/p/jouleunit.
  20. Wilke, C., Richly, S., Pschel, G., Piechnick, C., Gtz, S., and Amann, U. (2012). Energy labels for mobile applications. In Workshop zur Entwicklung Energiebewusster Software, EEbS 2012.
  21. Witten, I. H., Frank, E., and Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 3 edition.
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Paper Citation


in Harvard Style

Rocheteau J., Gaillard V. and Belhaj L. (2014). How Green Are Java Best Coding Practices? . In Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-025-3, pages 235-246. DOI: 10.5220/0004808302350246


in Bibtex Style

@conference{smartgreens14,
author={Jérôme Rocheteau and Virginie Gaillard and Lamya Belhaj},
title={How Green Are Java Best Coding Practices?},
booktitle={Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},
year={2014},
pages={235-246},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004808302350246},
isbn={978-989-758-025-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - How Green Are Java Best Coding Practices?
SN - 978-989-758-025-3
AU - Rocheteau J.
AU - Gaillard V.
AU - Belhaj L.
PY - 2014
SP - 235
EP - 246
DO - 10.5220/0004808302350246