The Green Lab - Experimentation in Software Energy Efficiency (ICSE)
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The document discusses the challenges of energy efficiency in software, highlighting the disconnect between energy-inefficient software and hardware optimizations. It presents a case study on energy hotspots in virtual machine environments and outlines a research strategy for identifying and validating these hotspots. The document emphasizes the cyclical nature of empirical software engineering research, providing insights into effective experimentation for improving software energy efficiency.
![Software is energy-inefficient
Hardware
Energy-
unaware
software
Hardware optimizations are
negated by software
inefficiencies [cf. Wirth’
Law]
….
© Patricia Lago 2014](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-4-320.jpg)
![[Research survey on
Industrial Energy
Management. M. Littlefield,
LNS Research, 2013]
[The energy efficiency
potential of cloud-based
software: A US case study.
Tech. rep., Berkeley,
California, 2013]
Motivation](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-9-320.jpg)
![Knowledge for developers
[Procaccianti et al., Empirical Evaluation of Best Practices for Energy-Efficient Software
Development, Springer Empirical Software Engineering, under preparation, 2015]
[Gude & Lago, Best Practices for Energy-Efficient Software, wiki.cs.vu.nl/green_software]
© Patricia Lago 2014](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-11-320.jpg)
![Knowledge for designers and architects /1
[Procaccianti et al., A Systematic Literature Review on Energy Efficiency in Cloud Software Architectures, Sustainable Computing:
Informatics and Systems, Elsevier, 2014]
© Patricia Lago 2014
Cloud FederationSelf Adaptation Energy Monitoring](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-12-320.jpg)
![Knowledge for designers and architects /2
[Procaccianti et al., A Systematic Literature Review on Energy
Efficiency in Cloud Software Architectures, Sustainable Computing:
Informatics and Systems, Elsevier, 2014]
[Procaccianti et al., Green Architectural Tactics for the Cloud,
IEEE/IFIP WICSA, 2014]
[Lewis & Lago, A Catalog of Architectural Tactics for Cyber-
Foraging, ACM SIGSOFT QoSA, 2015]
© Patricia Lago 2014](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-13-320.jpg)
![Open Knowledge
[Gu and Lago, Estimating the economic value of reusable green ICT
practices, ICSR, Springer, 2013]
ICT and Sustainability
http://greenpractice.few.vu.nl
http://thegreenpractitioner.amsterdamdatascience.nl
© Patricia Lago 2014](https://image.slidesharecdn.com/tb-presentationfinal-150519130229-lva1-app6892/85/The-Green-Lab-Experimentation-in-Software-Energy-Efficiency-ICSE-14-320.jpg)
The Green Lab - Experimentation in Software Energy Efficiency (ICSE)
- 1. The Green Lab Experimentation in Software Energy Efficiency ICSE 2015 Technical Briefing 19th May, 2015 Giuseppe Procaccianti, Patricia Lago, VU University Amsterdam, ND Antonio Vetrò, Daniel Méndez Fernández, TU München, GER Roel Wieringa, University of Twente, ND
- 4. Software is energy-inefficient Hardware Energy- unaware software Hardware optimizations are negated by software inefficiencies [cf. Wirth’ Law] …. © Patricia Lago 2014
- 5. “Software: it's a gas” -Nathan Myrvhold, Microsoft Research
- 8. Source: A major IT manufacturing company
- 9. [Research survey on Industrial Energy Management. M. Littlefield, LNS Research, 2013] [The energy efficiency potential of cloud-based software: A US case study. Tech. rep., Berkeley, California, 2013] Motivation
- 10. State-of-the-Art: Software Energy Impact
- 11. Knowledge for developers [Procaccianti et al., Empirical Evaluation of Best Practices for Energy-Efficient Software Development, Springer Empirical Software Engineering, under preparation, 2015] [Gude & Lago, Best Practices for Energy-Efficient Software, wiki.cs.vu.nl/green_software] © Patricia Lago 2014
- 12. Knowledge for designers and architects /1 [Procaccianti et al., A Systematic Literature Review on Energy Efficiency in Cloud Software Architectures, Sustainable Computing: Informatics and Systems, Elsevier, 2014] © Patricia Lago 2014 Cloud FederationSelf Adaptation Energy Monitoring
- 13. Knowledge for designers and architects /2 [Procaccianti et al., A Systematic Literature Review on Energy Efficiency in Cloud Software Architectures, Sustainable Computing: Informatics and Systems, Elsevier, 2014] [Procaccianti et al., Green Architectural Tactics for the Cloud, IEEE/IFIP WICSA, 2014] [Lewis & Lago, A Catalog of Architectural Tactics for Cyber- Foraging, ACM SIGSOFT QoSA, 2015] © Patricia Lago 2014
- 14. Open Knowledge [Gu and Lago, Estimating the economic value of reusable green ICT practices, ICSR, Springer, 2013] ICT and Sustainability http://greenpractice.few.vu.nl http://thegreenpractitioner.amsterdamdatascience.nl © Patricia Lago 2014
- 15. Software Energy Efficiency is odd
- 16. Software Energy Efficiency is odd • Examples: – Diminishing returns of adding more resources Source: Harizopoulos, S., Shah, M. A., Meza, J. & Ranganathan, P. Energy Efficiency: The New Holy Grail of Data Management Systems Research. CoRR abs/0909.1784, (2009).
- 17. Software Energy Efficiency is odd • Examples: – Algorithm design Source: Harizopoulos, S., Shah, M. A., Meza, J. & Ranganathan, P. Energy Efficiency: The New Holy Grail of Data Management Systems Research. CoRR abs/0909.1784, (2009).
- 18. Software Energy Efficiency is odd • Examples: – Third-party Software updates Source: Cameron, K. W. Energy Oddities, Part 2: Why Green Computing Is Odd. Computer 46, 90–93 (2013)
- 19. Hotspot Elements or properties, at any level of abstraction of the system architecture, that have a measurable and significant impact on energy consumption.
- 20. Case study
- 21. The VMware benchmark (VMmark)
- 22. Example – Energy Hotspot / measurements • VM energy estimation is quite reliable • Systematic error of about ~10 Watts Estimation Measure 100120140 Watt
- 23. Example – Energy Hotspot visualization / measurements • If we plot the measure -10 watts you can see it approximates well 100120140160 Timestamp Watt 15:40:00 15:46:00 15:52:00 15:58:00 16:04:00 16:10:00 Estimation Measure
- 24. Example – Energy Hotspot visualization • VMs over time: what happened in Phase 2? 010203040 Timestamp Watt 15:56:00 15:57:00 15:58:00 15:59:00 16:00:00 16:01:00 16:02:00 DB WS1 WS2 WS3
- 25. Phase 1: Hotspots Identification • Measure energy usage in the field • Identify patterns in the data • Explain these by underlying mechanisms Phase 2: Hotspots validation • Try to reproduce these patterns by simulating these mechanisms in the lab. • Investigate in the lab how different mechanisms may interact • Search for evidence of those patterns in the field • Integrate this into theories about energy usage • From this knowledge, identify recommendations for reducing energy usage Research Strategy
- 26. Generalization and explanation Application and testing in the lab and in the field The Approach
- 28. Tool: Spotfire Tibco (http://spotfire.tibco.com/) Hands-on session Data: http://www.s2group.cs.vu.nl/green-lab/ (Click on ICSE Technical Briefing – online package)
- 30. Looking back (briefly): Principles of scientific working Measurements Patterns Theories Identify patterns Generalisation and explanation Hypothetical mechanisms Explain the patterns Integrate into theories Application and testing in the lab and in the field Study Population (entities from the theoretical population) Theoretical Population (all entities that we want to generalise about)
- 31. Empirical SE is conducted in a cyclic manner Engineering Cycle Treatment implementation Implementation evaluation / Problem investigation Treatment designDesign validation
- 32. Scaling up by iterating through the engineering cycle Engineering Cycle Engineering Cycle . . . . . . Idealised assumptions Realistic assumptions • Evidence is gathered in a cyclic (step by step) manner • With each iteration, we – Narrow down our observations – Increase the precision of our instruments and, thus, measurements – Increase the validity of our conclusions
- 33. Levels of evidence + For - Against Strong evidence Evidence Circumstantial evidence Third-party claim First or second part claim Strong evidence Circumstantial evidence Third-party claim Evidence First or second part claim Further reading: Wohlin. An Evidence Profile for Software Engineering Research and Practice
- 34. What does this mean for Experimentation in Software Energy Efficiency? 1 1 2
- 35. What does this mean for Experimentation in Software Energy Efficiency? Objectives in experimental settings* • Hypothesis-driven exploration • Statistical significance • Control • Blocked subject assignment • Balanced subject groups • Contextualisation • Cause-effect analyses • Randomised assignments • Replicability • Competing alternatives * Don’t blame us, Claes Wohlin said so… ;-)
- 36. Implications on Principles in Experimental SE Relax rigor in favour of pragmatism in initial stages • Hypothesis-driven exploration • Statistical significance • Control • Blocked subject assignment • Balanced subject groups • Contextualisation • Cause-effect analyses • Randomised assignments • Replicability • Competing alternatives Relax Stress
- 37. Thank you!