Spirals

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Lionel Seinturier

Presentation of the Spirals project-team

Wissenschaft

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

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

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

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

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

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

2. Scientific Objectives
1.  Self-healing
Mining software artefacts to smartly evolve software systems
•  Design healing
•  Code healing
•  Run-time healing
7L. Seinturier

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

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

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

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

APISENSE® http://www.crowdify.com
L. Seinturier 12

Makitoo http://www.makitoo.com
L. Seinturier 13

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

Saloon Configure and deploy for cloud
L. Seinturier 15

OCCIware Cloud ressource management
L. Seinturier 16

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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Spirals

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 L. Seinturier 2 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 L. Seinturier 3

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. 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. 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. 2. Scientific Objectives 1.  Self-healing Mining software artefacts to smartly evolve software systems •  Design healing •  Code healing •  Run-time healing 7L. Seinturier

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. 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. 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. 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. APISENSE® http://www.crowdify.com L. Seinturier 12

13. Makitoo http://www.makitoo.com L. Seinturier 13

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. Saloon Configure and deploy for cloud L. Seinturier 15

16. OCCIware Cloud ressource management L. Seinturier 16

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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