High-confidence Software for Cyber Physical Systems
Drexel University Philadephia, PA
Vanderbilt University Nashville, Tennessee
Aniruddha Gokhale*, Sherif Abdelwahed
{a.gokhale,s.abdelwahed}@vanderbilt.edu
www.dre.vanderbilt.edu/~gokhalewww.isis.vanderbilt.edu/~sherif
*Proposed research ideas are based partly on prior work done for the DARPA PCES and ARMS programs.
Nagarajan [email protected].
eduwww.ece.drexel.edu/
~kandasamy
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• Network-centric, dynamic, large-scale “systems of systems”
• Service-oriented architecture of distributed collaborating services
• Stringent simultaneous QoS demands, e.g., “never die,” time-critical, secure.
• Highly diverse, complex, integrated & autonomous application domains
• On demand computing needs
Traits of Cyber Physical Systems
Key Requirements for High Confidence Software
• Trustworthiness - delivering multiple, simultaneous QoS
• Autonomicity – self healing, self configuring, self optimizing
• Analyzability – amenable to validation and verification
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Step 1. Algorithms for Distributed Control & Diagnosis
• System management tasks are posed as control/optimization problems and solved under dynamic and uncertain operating conditions
• Online parameter tuning and model-learning techniques can be integrated within the control framework to improve the quality of partially specified system models as well as adapt to changes in the system model itself over time
• Diagnosis algorithms will detect, isolate, and estimate the state of corrupted hardware and software components using concepts from continuous and discrete-event diagnosis, and consistency-based causality analysis.
Enterprise computing system
PerformanceOptimizer
System model (M)
Learning structure
Environment Inputs (i)
Estimators
Estimated inputs
System response (r)
System state (x)
Control decisions (d)
Control inputs
State feedback
rdi
i
x
r’System Model (M’)
Faultdetection/recovery
Recovery/reconfiguration actions
Model-based control
Model-based diagnosis
Enterprise computing system
PerformanceOptimizer
System model (M)
Learning structure
Environment Inputs (i)
Estimators
Estimated inputs
System response (r)
System state (x)
Control decisions (d)
Control inputs
State feedback
rdi
i
x
r’System Model (M’)
Faultdetection/recovery
Recovery/reconfiguration actions
Model-based control
Model-based diagnosis
Focus is on developing algorithms to realize incorruptible and self-healing CPSs via a combination of control and diagnostics
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Step 2. MDE Tool Chainwww.dre.vanderbilt.edu/cosmic
www.dre.vanderbilt.edu/CIAO
Modeling toolsModeling tools
Model Model InformationInformation
Domain, Deployment, SRG, FOU,
Connection QoS, Security
injectionReplica Placement,
Bandwidth allocation, Security
model GeneratorsGenerators
Augmented Augmented Deployment Deployment
PlanPlan
Middleware Bus
Container
…
SecurityReplication TransactionPersistence
Container
… …• Capture trustworthiness dimensions
(e.g.,RT, FT and Security) via DSMLs• Generative programming approach that
uses QoS specs, control algorithms and middleware features to synthesize CPS artifacts
Focus is on resolving accidental complexities and automating system configuration, deployment, adaptation and conducting analyses.
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Step 3. Trustworthy Middleware Framework• Decouple system adaptation policy from system application code & allow
them to be changed independently from each other
• Decouple system deployment framework & middleware from core system infrastructure to allow CPSs to be dynamically reconfigurable
System ObserversSystem ObserversSystem Condition
Observers
System Deployment Agents
System D&C Actors & Middleware
Adaptation Planner
ControlAlgorithmControlAlgorithm
AdaptationPlan
SystemConditions
Running Systems
Control and diagnosticsSelf healing
Self configuring & optimizing
Reflective capabilities
Focus is on realizing a scalable, trustworthy runtime environment.
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Step 4. System Execution Modeling Tools
“What if” analysis
Validate design conformance
Validate design rules
Focus is on continuous QoS integration and validation via design-time analysis and automated empirical testing/validation
www.dre.vanderbilt.edu/cosmic
www.dre.vanderbilt.edu/CUTS