Recommended
Recommended
More Related Content
Similar to ICAC2017 Keynote by Nelly Bencomo
Similar to ICAC2017 Keynote by Nelly Bencomo (20)
Recently uploaded
Recently uploaded (20)
ICAC2017 Keynote by Nelly Bencomo
- 5. Aston Lab for Intelligent Collec3ves Engineering View from my window in my office at Aston University
- 6. Introducing Myself • Fundamental research in self-adap1ve systems – Decision-making under uncertainty – SoHware Engineering techniques including the use of models@run.1me – Inten1on-aware and requirements-aware systems • A fundamental aspect of life – I like hiking and enjoying my own run 1me (I enjoy running!)
- 16. models@run.time : body of work IEEE Computer: Special issue on Models@run.time, 2009 Computing: Special issue on Models@run.time, 2013 Workshop models@run.time at MODELS (12th edition since 2006) http://st.inf.tu-dresden.de/MRT17/?site=editions Workshop models@run.time at ICAC (2nd edition) Dagstuhl Seminar – 2011 Book Models@run.time - Foundations, Applications, and Roadmaps - 2014
- 17. The Problem – Implementa1on Gap • A problem-implementa1on gap exists when a developer implements soHware solu1ons to problems using abstrac1ons that are at a lower level than those used to express the problem. – Exacerbated in autonomous systems • “How can modeling techniques be used to tame the complexity of bridging the gap between the problem domain and the so:ware implementa;on domain?” [FOSE '07] - [FOSE '07] : France, R., Rumpe, B. (2007). Model-driven Development of Complex SoXware: A Research Roadmap. In 2007 Future of SoXware Engineering, IEEE - [ieee 2009] : Editorial IEEE Computer: Special issue on Models@run.3me, Blair G., Bencomo N., and France R., October, 2009
- 19. The Problem-Implementa1on Gap Revisited today in this talk • In more than 10 years of research in models@run.1me, has this problem- implementa1on gap narrowed? • What tools and techniques have been developed? • What have we learned? Next, I will explained my own contribu1ons (with other colleagues)
- 20. 1- From Design-Time to Run-Time: Emergent Middleware • We considered middleware as a run-1me en1ty that is generated automa1cally to meet the needs of the current context and needs of applica1ons • We observe, learn, synthesise and deploy a middleware instan1a1on dynamically CORBA service Web Service Interoperability Solu1on Monitor & Learn Monitor & Learn Generated BINDING Synthesize Emergent middleware [Compu3ng 2013]: The Role of Models@run.1me in Suppor1ng Synthesis of Emergent Middleware, Bencomo, Bennaceur, Grace, Blair, Issarny
- 21. Models@run.time to Support Synthesis of Emergent Middleware [Compu3ng 2013]: The Role of Models@run.1me in Suppor1ng Synthesis of Emergent Middleware, Bencomo, Bennaceur, Grace, Blair, Issarny EU ConnectProject
- 22. Models@run.time to Support Synthesis of Emergent Middleware [Compu3ng 2013]: The Role of Models@run.1me in Suppor1ng Synthesis of Emergent Middleware, Bencomo, Bennaceur, Grace, Blair, Issarny EU ConnectProject
- 23. We have demonstrated • Run1me models do not have to be conceived at design only • Run1me models can be “learned” during execu1on • Run1me models used to dynamically generated code
- 24. Mo1va1on- The system needs to explain what is going on • Emerging behaviour means that the system may surprise its customers and/or developers. • Lack of understanding can mean that users may cease to use a self-adap1ve system • Self-adap3ve systems need to have self-explana3on capabili3es [TAAS 2014] :"Self-explana3on in Adap3ve Systems based on Run3me Goal-based Models”, Bencomo, Welsh, Sawyer, Whifle, Transac3ons on Computa3onal Collec3ve Intelligence, TAAS, 2014
- 25. Mo1va1on- The system needs to explain what is going on • Emerging behaviour means that the system may surprise its customers and/or developers. • Lack of understanding can mean that users may cease to use a self-adap1ve system • Self-adap3ve systems need to have self-explana3on capabili3es [TAAS 2014] :"Self-explana3on in Adap3ve Systems based on Run3me Goal-based Models”, Bencomo, Welsh, Sawyer, Whifle, Transac3ons on Computa3onal Collec3ve Intelligence, TAAS, 2014
- 26. • The system has adapted to a new configura1on – what? how? why? • The systems has crashed when trying to adapt – what? how? why? • The user may want to understand the system in terms of requirements or their own language Very (sad) user
- 27. • The system has adapted to a new configura1on – what? how? why? • The systems has crashed when trying to adapt – what? how? why? • The user may want to understand the system in terms of requirements or their own language Very (sad) user A well-known problem with self-adap1ve autonomous systems is that users may not understand or trust them
- 28. • The system has adapted to a new configura1on – what? how? why? • The systems has crashed when trying to adapt – what? how? why? • The user may want to understand the system in terms of requirements or their own language Very (sad) user A well-known problem with self-adap1ve autonomous systems is that users may not understand or trust them The system needs to offer explana1ons using the language of the stake holder
- 35. Uncertainty : a big challenge • Some tend to avoid it • We need to embrace Uncertainty [Le1er,2014]
- 36. Uncertainty : a big challenge • Some tend to avoid it • We need to embrace Uncertainty [Le1er,2014]
- 39. U Evidence Collect Data Frequently (D) Energy Efficiency (E) Decision SP FH 22 Bayesian Learning to support decision making for self- adap1ng systems using Dynamic Decision Networks Decisions (goal realizations) SP: Clean when Empty SH: Clean at Night Chance node) (Softgoals - non functional requirements) M : Minimize Energy Cost A : Avoid Tripping Hazard collect data Shortest path (SP) Fewest Hops (FH) energy Efficiency (E) collect data frequently (D) ++ -- ++ -- P(D|SP) [Bencomo and Belaggoun, REFSQ 2013 – Best Paper Award]
- 40. U Evidence Collect Data Frequently (D) Energy Efficiency (E) Decision SP FH 22 Dynamic Decision Networks frame the decision-making of a self-adap1ng system Decisions (goal realizations) SP: Clean when Empty SH: Clean at Night Chance node) (Softgoals - non functional requirements) M : Minimize Energy Cost A : Avoid Tripping Hazard P(D|SP) [REFSQ 2013] – Best Paper Award Bencomo et all [SEAMS 2013] Bencomo, Belaggoun, Issarny [SEAMS 2015] Hassan, Bencomo, Bahsoon EUj = EU(dj | e) = P(xi i ∑ | e,dj )U(xi | dj ) j = 1,2... • Types of nodes: • Chance nodes: labeled by random variables Xi that represent the states of the world (NFRs) • Decision nodes: with the set of configura1ons • U;lity nodes: that state the preferences about the states of the world • Evidence nodes: to denote the observable variables The condi3onal probabili3es quan3fy the effects of decisions on states of the world
- 41. X1(t) X(t+1) D(t) D(t+1) U(t+1)U(t) E(t) E(t+1) Evidence depends on state X2 X2 …. …. …. Time t Time t+1 Time t+n Dynamics Decision Networks (DDNs) EUj = EU(dj | e) = P(xi i ∑ | e,dj )U(xi | dj ) j = 1,2...
- 47. Remote Data Mirroring (1) Copies of important data are stored at one or more secondary loca1ons Goal: Protect data against loss and unavailability Case Study • Design choices • Remote mirroring protocols e.g. Minimum spanning tree (MST) vs Redundant topology (RT) (1) “Relaxing claims:Coping with uncertainty while evalua3ng assump3ons at run 3me,” A. Ramirez, B. Cheng, N. Bencomo, and P. Sawyer, ACM/IEEE Int. Conference on Model Driven Engineering Languages & Systems MODELS, 2012.
- 52. What has been done [Garcia&Bencomo Poster RE 2016, AIRE Workshop 2016] [Garcia&Bencomo RE-NEXT 2017], CibSE 2017 • We have used the concept of Bayesian surprise to measure how observed data modify, during run1me, previous assump1ons of the world. • The final result is a befer informed decision making by enabling the re-appraisal and update of the run3me models according to evidence gathered from the opera1onal environment. • ARRoW: Automa1c Run1me Reappraisal of Weights
- 54. A final word: Raising the Level of Abstrac1on from Systems to Applica3ons • Because its behaviour is emergent and uncertainty, an autonomic system needs to promote confidence and resolve any surprise. • How do we raise the level abstrac1on so programmers are working with applica1on logic rather than systems logic? • How is the ICAC community working on bridging this gap? • Need of Mul3disciplinary Collabora3ons
- 55. Acknowledgments • PhD/Master Students – Luis Garcia Paucar (PhD Student Aston University, 2015-) – Amel Belaggoun (Master Student, Inria 2013) • Collaborators – Gordon Blair (Lancaster University, UK) – BeZy Cheng (Michigan State University, US) – Valerie Issarny (Inria@SiliconValley) – Robert France (RIP) – Gabby my daughter (for her drawings)