Presentation of the Spirals project-team

1. Lionel Seinturier
SPIRALS
Self-adaptation for distributed
services and large software systems
Joint project-team Inria / University of Lille
16 June 2016

2. 1. Members
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Laurence
Duchien
Philippe
Merle
Martin
Monperrus
Romain
Rouvoy
Walter
Rudametkin
Lionel
Seinturier

3. 1. Members
6 permanents researchers
1 post-doc researcher
10 PhD students
4 research engineers
10 nationalities: Mex-US, Ch, Sp, It, Tun, Be, Ben,
Li, Ye, Fr
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4. 2. Scientific Objectives
Distributed Systems & Middleware
2 scientific foundations
• middleware
• software engineering
2 target environments
• mobile computing
• cloud computing
Application domains
• retail
• telecom
• home automation
• cyber-physical systems
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5. 2. Scientific Objectives
Self-adaptive distributed systems
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New forms of complexity
• adapting software syst to a vast diversity of distributed platforms
• adaptation impacts the entire software development lifecycle

6. 2. Scientific Objectives
1. Self-healing
Mining software artefacts to smartly evolve software systems
2. Self-optimization
Sharing runtime behaviors to continously adapt softw systems
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7. 2. Scientific Objectives
1. Self-healing
Mining software artefacts to smartly evolve software systems
• Design healing
• Code healing
• Run-time healing
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8. 2. Scientific Objectives
1. Self-healing
Mining software artefacts to smartly evolve systems and to
reach the next level of automated adaptation
• Recommendation systems for healing
- bug oriented: localization, characterization, fix generation
e.g. first results: catalog of 18 bug fix patterns
- design oriented: API usages, rules for good design
• Run-time healing
- targeted properties: resilience, repair
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9. 2. Scientific Objectives
2. Self-optimization
Sharing runtime behaviors to continously adapt softw systems
• Monitoring and understanding software systems in the large
• Collaborative decision-making approaches
• Smart propagation of reconfigurations in the large
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Implementation
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10. 2. Scientific Objectives
2. Self-optimization
Sharing runtime behaviors to continously adapt softw systems
• feed-back control loops as 1st class elements in software
systems
• quality of service (e.g. energy monitoring)
• distributed algorithms
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11. 3. Software Systems and Transfer
1. APISENSE® http://www.crowdify.com
2. Makitoo http://www.makitoo.com
3. PowerAPI http://www.powerapi.org
4. Saloon
5. OCCIware
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12. APISENSE® http://www.crowdify.com
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13. Makitoo http://www.makitoo.com
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14. PowerAPI http://www.powerapi.org
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0 10 20 30 40 50 60
Power(W)
Time (sec)
x264
freqmine
PowerVM
Idle power
PowerSpy
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0 200 400 600 800 1000 1200 1400 1600 1800
Powerconsumption(W)
Time (s)
PowerVM PowerSpy
Building software-defined power meters
• Support for various input sources
• Hardware (PowerSpy, RAPL, APC)
• Software (ProcFS, Sigar, libpfm4)
• Support for various power models
• Parameter-based models (CMOS)
• Learning-based models (app-agnostic)
• Support for various output channels
• Console, plots, streaming, network
• Support for various control interfaces
• GUI, web, filesystem
• OSS under GNU Affero General Public
License
• Scala & Akka

15. Saloon Configure and deploy for cloud
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16. OCCIware Cloud ressource management
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17. Spirals in a nutshell
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2 research objectives
§ self-healing
§ self-optimization
2 target environments
§ mobile computing
§ cloud computing
2 scientific foundations
§ middleware
§ software engineering