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![Related Work
I. Epifani, C. Ghezzi, R. Mirandola, G. Tamburrelli. “Modelevolution by run-time
parameteradaptation”. ICSE '09
[Firelli et al. 2011] [Cardenilli et al 2011] [Calinescu et al. 2011] [Calinescu et al. 2012]
• [Epifani et al. 2009]
– Humans estimations are seldom correct + in
dynamic environments, the value of parameters
may change over time
• keeping models alive during runtime and maintaining
updated the parameters by feeding a bayesian
estimator with data collected from the running system
• Parameters always are average values
– Problem: humans cannot constraint properly their
functional goals
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Requirements, Design and Data Repositories
1) Self-adaptive service-based systems need to automatically maintain specification models during runtime to account for uncertainty from changing environments and goals. 2) As service properties change over time, specification models may become invalid representations of original requirements, missing violations. 3) The authors propose using a constraint language based on computing with words to specify non-functional constraints linguistically rather than numerically, along with an architecture to assess abstract specification models and detect concept drifts in service properties.
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Requirements, Design and Data Repositories
- 1. Addressing the QoSdrift in Specification Models of Self-adaptive service-based systems Romina Torres (1), Nelly Bencomo(2), Hernán Astudillo(1) (1)Universidad Técnica Federico Santa María (2)INRIA Paris-Rocquencourt 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE – Mayo 2012) 1
- 2. CONTEXT Self-adaptive Service basedSystems 2
- 3. Introduction • Dynamic adaptivesystems must be able to adapt themselves during runtime to cope with the uncertainty associated with changes in their goals or in the environment in which they operate WITH NO (OR LIMITED) HUMAN INTERVENTION. 3
- 4. Requirements (R): Send theweather prediction for the weekend to the user wherever he is as fast as possible. Functional goals and non functional constraints Specifications (S) M. Mannion, B.Keepence. 1995. SMART requirements. SIGSOFT Softw. Eng. Notes, or GQM 4
- 5. Specifications (S) Architectural Configuration(C) f(S)=C’ During Runtime proactive reactive 5 P. Zaveand M. Jackson, “Fourdarkcornersofrequirementsengineering,” 1997 ACM Txson SE &Metho Adaptation
- 6. PROBLEM Perceptual Systems underchanging environments 6
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- 9. Problem • Cause: – Perceptualsystems may be degraded when environments are highly changing • Implication – Specification models (S) may be degraded due to the highly changing service market • Self-adaptive Service-based systems may miss requirements violations because S is not anymore a valid representation of R. D. Hall, “Automaticparameterregulationofperceptualsystems,” ImageandVisionComputing, vol. 24, no. 8, pp. 870 – 881, 2006. 9
- 10. The natural solutionto the challenge • Humans should regularly and manually maintain their specification models in order to ensure they are a valid representation of their requirements during runtime 10
- 11. But… • Dynamic adaptivesystems must be able to adapt themselves during runtime to cope with the uncertainty associated with changes in their goals or in the environment in which they operate WITH NO (OR LIMITED) HUMAN INTERVENTION. • Humans cannot be aware of a huge and highly dynamic operating environment (partial views) 11
- 12. The real requiredsolution • Specification models must be automatically maintained during runtime in order to maintain them as valid representations of the humans requirements 12
- 13. RELATED WORK Keeping alivemodels 13
- 14. Related Work I. Epifani,C. Ghezzi, R. Mirandola, G. Tamburrelli. “Modelevolution by run-time parameteradaptation”. ICSE '09 [Firelli et al. 2011] [Cardenilli et al 2011] [Calinescu et al. 2011] [Calinescu et al. 2012] • [Epifani et al. 2009] – Humans estimations are seldom correct + in dynamic environments, the value of parameters may change over time • keeping models alive during runtime and maintaining updated the parameters by feeding a bayesian estimator with data collected from the running system • Parameters always are average values – Problem: humans cannot constraint properly their functional goals 14
- 15. PROPROSAL Computing with words+ strategy to detect drifts on words 15
- 16. Our proposal • Aconstraint language for humans to specify the NFCs in the specification models using concepts instead of numerical ranges. • Necessarily we also need – An architecture capable to assess “abstract specification models” – An infrastructure capable to obtain from the NFPs of the offerings of the service market the numerical meaning of the concepts. • Initial values • Each time there is enough evidence a drift has occurred in the NFPs 16
- 17. Preliminaries • Computing withwords (CWW) • Words instead of numbers for computing and reasoning • Words constrain linguistic variables – Fuzzy sets LoftiZadeh. “FuzzyLogic = ComputingwithWords”. IEEE TxsonFuzzy Systems 1996 Language to define NFCs 17
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- 19. Preliminaries • Specification modelsbecome linguistic decisions models • How do we assess them? – Perceptual Computer – an architecture for CWW J. M. Mendel, D. Wu. “ChallengesforPerceptualComputerApplicationsand How TheyWere Overcome”. IEEE ComputationalIntelligence Magazine, 2012. L. Martínez, D. Ruan, F. Herrera, E. Herrera-Viedma, P. P. Wang: Linguistic decision making: Tools and applications. Inf. Sci. 179(14): 2297-2298 (2009) 19
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- 21. Preliminaries • Concept driftoccurswhenthecontextshifts induce changes in thetargetconcept • Functionally-equivalentservices are continuouslycompeting in termsofQoStoachievetheirownsurvivalgoal: to be selected • Perceptionsaboutwhatmeanseachconceptofea ch NFP ofeachfunctionalgroupofserviceschange 21
- 22. Preliminaries G. Leng, X.-J.Zeng, and J. A. Keane, “A hybridlearningalgorithmwith a similarity- basedpruningstrategyforself-adaptiveneuro-fuzzysystems,” AppliedSoftComputing 2009. – randomnoise, – randomtrends (gradual changes), – randomsubstitutions (abruptchanges), – orsystematictrends We record also the trend of the drift and we base our decision on the trend history of the past “potential” drifts 22 Outliers?
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- 26. Validation Strategy • PrototypeImplementation of Adaptive • Dataset from programmableweb.com – 10 functional categories, 2 non-functional properties, over 150 services (3000 “words”). – 5 SBSs clients subscribing their adaptation needs – 3 QoS drifts synthetically simulated in a window of 10 new NFP certifications. • We were able to detect requirements violations where configurations become relative violators because others functionally-equivalent competitors improve their QoS. => reduction of false negative error 26
- 27. CONCLUSIONS & FUTUREWORK 27
- 28. Conclusions • Current systemsare being released too fragile – Lack of self-monitoring and self-repairing capabilities – Lack of awareness – Self-adaptive Systems demands systems “maintainability” be performed automatically – But even manually it is performed poorly • So, still there is a lot of work on the SE side 28
- 29. Ongoing and futurework • Tuning Adaptive • False positives? • Stress testing - Scalability • Sensitivity analysis - Parameters tradeoff • New techniques to mitigate the obsolescence of models during runtime • Release dataset of measurements of NFPs of services during a frame of time where several QoS drifts may occur. 29
- 30. Acknowledgements This work waspartially funded by UTFSM DGIP (PIIC and 241167), BASAL FB0821, Conicyt Chile, the EU Connect project and the EU Marie Curie Requirements@runtime. 30