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The challenge of
putting software
sustainability research
into practice
Sandro Kreten
Who Am I?
Head of Technology
@capacura - Europe's most innovative
impact investor
Dr. rer. nat. in the field of modeling and
implementation of methods for the
energy-efficient use of cloud
technologies
Co-development of Blue Angel for
resource-efficient software in
cooperation with the Ökoinstitut and the
Environmental Campus Birkenfeld
10 years of experience in software
development
Several teaching positions at German
universities (of applied science)
Dr. Sandro Kreten
Who Am I?
The capacura Portfolio
Founding: 04.2018
Investment Focus: SDG 3, 4, 13*
Number of Startups: 21 Startups
Investment Volume: 13+ Mio.€
Portfolio CAGR: 22,7%/p.a.
capacura Startups
*SDG 3 = good health and well-being; SDG 4 = quality education; SDG 13 = climate action
Some Insights
… and Definitions
Understanding Green Software
Software Sustainability
Economic
Software
Sustainability
Green
Software
Green
BY
Software
Environmental Sustainability
Human
Software
Sustainability
(Green Software)
IN
Software Sustainability (2001). Calero et al. Springer Nature Switzerland
Some Research Results
Criteria for ressource and energy efficient
software
Methodoligies
Measurement Tools
Standard Usage
Scenarios
Recommended
actions for
specific cases
Other Tools
IT
Governance
Human
Factors
ISO/IEC-Norm 14756
Information technology — Measurement and rating
of performance of computer-based software
systems
DIN EN 303470:2018-10
Metrics and Measurements of Servers
ISO/IEC CD 23544
Application Platform Energy Effectiveness
ISO/IEC 33000 Family
Process assessment in the the information
technology domain
Putting software
sustainability research
into practice…
Why is it so hard??
Key Problems
Complexity
Hands-On
Experience
Great Concepts
Cooperation
Needs of Target
Groups
• Software is complex
• Combined with infrastructure it is a
mess
• Scope often too large to work
comparatively
• “What is the most efficient framework for
machine learning?“
• Finding the “right“ topics is difficult
Complexity
DevOps
Platform Engineer
DevSecOps
CloudOps
etc.
Example
Researcher
Lack of hands-on experience
• Methodical competence is available
• Lack of practical experience
• Real life examples often have to be learned and evaluated
• Depth of knowledge and experience of an expert often not
replaceable
Implementation and development of goals often cannot
keep up with the speed of development
Great Concepts
• Papers with a good impact factor often require larger concepts
• Practical results need to be justified more often
• Generalization can lead to problems
“Students know which
programming languages
are more environmentally
friendly“
Example
Context sensitivity is
extremely important to
explore useful results
Needs of Target Groups
• Requirements and needs must be specified from the economy
• Quicker results are needed
• Implementation must be cost-effective and result should save costs
• Results must be easy to understand
Cooperation, Acceptance and
Exchange of Results
• The acceptance of the content was very difficult until recently
• Open interfaces for monitoring the resources should be made
available to make measurements easier
• Sometimes you need to tweak or hack
tools/frameworks/interpreters in order to find results
• More and easier collaborations are important, which is more than a
hurdle for funding but where it is really about collaboration
The good news is…
(even without perfect research results)
Results can already be used profitably
• Current research results create a basis for comparative work and for making the
right decisions
• blue angel criteria are a good starting point because of their holistic nature
• Models become more practical and can be applied especially in IT planning
• ISOs
• Especially in the area of cloud and data centers there are helpful insights and tools
• Auto Scalers
• Server Consilidation
• Common processes can already be helpful
• Code Audits
• Refactoring
• Preparing Monitoring and Measurement
• Open source projects to enhance ordinary Monitoring (Code Carbon, RAPL)
• The economy is becoming active. Companies already provide (often open)
solutions
A feasible example
A glimpse into the future
• The interest increases
• More developers make results available in the interests of transparency.
Comparisons become easier
• The number of recommendations for action is increasing but they need to be more
practical for developers
• Real Do‘s or Dont‘s are possible but only context sensitive
• Measurement environments become more feasible, less expensive and analysis
becomes automated
• First ML models reveal efficiency gaps
• Although some results already exist, cloud continues to offer very large points of
attack. The scaling effect of savings is simply much greater here.
Thank you for the
attention!
I will be happy to answer your questions!

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The challenge of putting software sustainability research into practice

  • 1. The challenge of putting software sustainability research into practice Sandro Kreten
  • 2. Who Am I? Head of Technology @capacura - Europe's most innovative impact investor Dr. rer. nat. in the field of modeling and implementation of methods for the energy-efficient use of cloud technologies Co-development of Blue Angel for resource-efficient software in cooperation with the Ökoinstitut and the Environmental Campus Birkenfeld 10 years of experience in software development Several teaching positions at German universities (of applied science) Dr. Sandro Kreten Who Am I?
  • 3. The capacura Portfolio Founding: 04.2018 Investment Focus: SDG 3, 4, 13* Number of Startups: 21 Startups Investment Volume: 13+ Mio.€ Portfolio CAGR: 22,7%/p.a. capacura Startups *SDG 3 = good health and well-being; SDG 4 = quality education; SDG 13 = climate action
  • 4. Some Insights … and Definitions
  • 5. Understanding Green Software Software Sustainability Economic Software Sustainability Green Software Green BY Software Environmental Sustainability Human Software Sustainability (Green Software) IN Software Sustainability (2001). Calero et al. Springer Nature Switzerland
  • 6. Some Research Results Criteria for ressource and energy efficient software Methodoligies Measurement Tools Standard Usage Scenarios Recommended actions for specific cases Other Tools IT Governance Human Factors ISO/IEC-Norm 14756 Information technology — Measurement and rating of performance of computer-based software systems DIN EN 303470:2018-10 Metrics and Measurements of Servers ISO/IEC CD 23544 Application Platform Energy Effectiveness ISO/IEC 33000 Family Process assessment in the the information technology domain
  • 7. Putting software sustainability research into practice… Why is it so hard??
  • 9. • Software is complex • Combined with infrastructure it is a mess • Scope often too large to work comparatively • “What is the most efficient framework for machine learning?“ • Finding the “right“ topics is difficult Complexity DevOps Platform Engineer DevSecOps CloudOps etc. Example Researcher
  • 10. Lack of hands-on experience • Methodical competence is available • Lack of practical experience • Real life examples often have to be learned and evaluated • Depth of knowledge and experience of an expert often not replaceable Implementation and development of goals often cannot keep up with the speed of development
  • 11. Great Concepts • Papers with a good impact factor often require larger concepts • Practical results need to be justified more often • Generalization can lead to problems
  • 12. “Students know which programming languages are more environmentally friendly“ Example
  • 13. Context sensitivity is extremely important to explore useful results
  • 14. Needs of Target Groups • Requirements and needs must be specified from the economy • Quicker results are needed • Implementation must be cost-effective and result should save costs • Results must be easy to understand
  • 15. Cooperation, Acceptance and Exchange of Results • The acceptance of the content was very difficult until recently • Open interfaces for monitoring the resources should be made available to make measurements easier • Sometimes you need to tweak or hack tools/frameworks/interpreters in order to find results • More and easier collaborations are important, which is more than a hurdle for funding but where it is really about collaboration
  • 16. The good news is… (even without perfect research results)
  • 17. Results can already be used profitably • Current research results create a basis for comparative work and for making the right decisions • blue angel criteria are a good starting point because of their holistic nature • Models become more practical and can be applied especially in IT planning • ISOs • Especially in the area of cloud and data centers there are helpful insights and tools • Auto Scalers • Server Consilidation • Common processes can already be helpful • Code Audits • Refactoring • Preparing Monitoring and Measurement • Open source projects to enhance ordinary Monitoring (Code Carbon, RAPL) • The economy is becoming active. Companies already provide (often open) solutions
  • 19.
  • 20.
  • 21.
  • 22. A glimpse into the future • The interest increases • More developers make results available in the interests of transparency. Comparisons become easier • The number of recommendations for action is increasing but they need to be more practical for developers • Real Do‘s or Dont‘s are possible but only context sensitive • Measurement environments become more feasible, less expensive and analysis becomes automated • First ML models reveal efficiency gaps • Although some results already exist, cloud continues to offer very large points of attack. The scaling effect of savings is simply much greater here.
  • 23. Thank you for the attention! I will be happy to answer your questions!