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Spirals

  1. 1. Lionel Seinturier SPIRALS Self-adaptation for distributed services and large software systems Joint project-team Inria / University of Lille 16 June 2016
  2. 2. 1. Members L. Seinturier 2 Laurence Duchien Philippe Merle Martin Monperrus Romain Rouvoy Walter Rudametkin Lionel Seinturier
  3. 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 L. Seinturier 3
  4. 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 L. Seinturier 4
  5. 5. 2. Scientific Objectives Self-adaptive distributed systems L. Seinturier 5 New forms of complexity •  adapting software syst to a vast diversity of distributed platforms •  adaptation impacts the entire software development lifecycle
  6. 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 L. Seinturier 6
  7. 7. 2. Scientific Objectives 1.  Self-healing Mining software artefacts to smartly evolve software systems •  Design healing •  Code healing •  Run-time healing 7L. Seinturier
  8. 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 8L. Seinturier
  9. 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 L. Seinturier 9 Implementation - 10
  10. 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 L. Seinturier 10
  11. 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 L. Seinturier 11
  12. 12. APISENSE® http://www.crowdify.com L. Seinturier 12
  13. 13. Makitoo http://www.makitoo.com L. Seinturier 13
  14. 14. PowerAPI http://www.powerapi.org L. Seinturier 14 80 90 100 110 120 130 140 0 10 20 30 40 50 60 Power(W) Time (sec) x264 freqmine PowerVM Idle power PowerSpy 20 30 40 50 60 70 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. 15. Saloon Configure and deploy for cloud L. Seinturier 15
  16. 16. OCCIware Cloud ressource management L. Seinturier 16
  17. 17. Spirals in a nutshell L. Seinturier 17 2 research objectives §  self-healing §  self-optimization 2 target environments §  mobile computing §  cloud computing 2 scientific foundations §  middleware §  software engineering

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