The document discusses the ASCENS project, which aims to build autonomic service-component ensembles through modeling languages, analysis techniques, and case studies. It summarizes results from the first year, including modeling languages for specifying ensemble knowledge and behavior, a system model for adaptive ensembles, and verification techniques. It also discusses challenges in ensuring reliability and confidence in large, heterogeneous ensembles operating in uncertain environments. Formal methods are proposed to specify reliability and confidentiality requirements and validate them through compositional verification and runtime monitoring techniques.

1. ASCENS
Autonomic Service Component Ensembles
Martin Wirsing and Matthias Hölzl
LMU Munich, Germany
AWARENESS Inter-Project Workshop
Bologna, 23-24 January, 2012
Future Emerging
Technologies
www.ascens-ist.eu

2. Ensembles
 Massive numbers of
nodes
 Extremely heterogeneous
 Complex interactions
between nodes
 Complex interactions
with humans or other
systems
 Operating in open and
non-deterministic
environments
 Dynamic adaptation to
 new requirements
 technologies and
 environmental conditions
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3. Approach
Service Component Ensemble (SCE) Engineering Emergence
reliable, predictable, self-adaptive,
balances efficient execution and Design and runtime control
flexible behaviour via of intended and
dynamic self-expression emergent behaviours,
static and dynamic
Service Component (SC) support from
self-aware, adaptive formal methods
secure and safe
based on
knowledge
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4. Approach
Service Component Ensemble (SCE) Case Studies
Science Clouds
Engineering Emergence
Swarm Robotics
Collaborating E-Vehicles
Tool Integr. Platform
Engineering
SCEL
SCE Language
(SCEL)
Knowledge
Correctness Self-Awareness
of SCs and SCEs Adaptation &
Foundational Models Self-Expression
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5. Aim of ASCENS
Build a coherent and integrated suite of models and techniques for
constructing
Autonomic Service-Component Ensembles
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6. Summary of Results Year 1
 Languages SCEL V1.0, BIP, KnowLang for modeling, analyzing and
specifying knowledge in ensembles
 System model GEM for adaptive and self-aware ensembles
 SOTA approach for specifying and analyzing adaptation requirements
 Verification techniques for qualitative and quantitative properties of
systems in uncertain environments
 Application requirements and system needs for case studies
 Robot ensembles, science cloud, collaborative e-vehicles
 Dissemination
 Website with blog
 50 publications
 2 summer schools (co-organized)
 ASCENS results taught in > 15 courses and tutorials
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7. Confidence and Reliability: Difficulties
 Ensembles are
 infinite state systems with varying members and environments
 concurrent systems suffering the state space explosion problem
 Uncertain environment and changing requirements
 Heterostatic ensembles (Klopf ~1975)
 maximize performance instead of simple goal satisfaction
 Confidence vs. confidentiality
 Confidence in reliability of a syteme = subjective reliabiity of the system
 Confidentiality = notion of information security
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8. Confidence and Reliability Ideas:
A Formal Methods Approach
 Specify confidentiality and reliability requirements
 in a (continuous/discrete time stochastic) temporal logic
 Validate and verify requirements for SC ensembles
 A posteriori verification
 Compositional techniques for
 specific properties (e.g. deadlock)
 specific architectures (e.g. controllers, from synchronous to asynchronous
communication, …)
 Correctness by supervised construction
 Correctness preserving architectural principles for system construction and
adaptation
 Runtime monitoring of global/emergent properties
 Predictive model analysis (at design and runtime)
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9. Confidence and Reliability
 Advice from other projects
 Trust models for measuring confidence and reliability
 Measures for confidence of/in (self-) aware components
 Designing emergence
 Challenges for measuring confidence
 Monitoring global and emergent properties
 Specifying and controlling adaptation
 Combining formal reasoning techniques with models of trust
 „Pervasive formal methods“ for enhancing confidence
 Interaction of global and local confidence
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