András Pataricza. Towards Dynamic Dependable Open Cyber-Physical Systems. Budapest University of Technology and Economics.

Transcription

1 Towards Dynamic Dependable Open Cyber-Physical Systems András Pataricza Budapest University of Technology and Economics

2 Contributors Dr. Tamás DABÓCZY Dr. Tamás KOVÁCSHÁZY Prof. Dr. Dániel VARRÓ Dr. István MAJZIK Dr. István RÁTH Dr. Ákos HORVÁTH Imre KOCSIS Ábel HEGEDŰS Ágnes SALÁNKI and many others "Cyber-Physical Systems" European Union, European Social Fund (TÁMOP C-11/1/KONV ),

3 Institut nn Prof. nn 3 Unlimited intelligence by the synergy of intelligence in the cyber space and ES interfacing the physical world CLOUD WEB Cyber CPS ES Comp. Physical THE NEW ERA: DYNAMIC CPS (?)

4 4 Motivation Reusable sensors Multiple, on-demand algorithms Intention Reality: record flood Solution A lot of s: Is it safe to come to Budapest? Fortunately: no request for Life west under your conference seat

5 Example Cameras on riverside Different applications concurrently using the same primary information Tasks can change according to time/season/requirements Identification of ships Monitoring the break-up of ice Monitoring the water level Monitoring the speed of flood Pollution check Supervision of hostile entrance to the ship

6 6 The design triangle WEB Cyber CPS CLOUD Comp. ES Physical

7 Dynamic cyber-physical systems Problem Service Solution pattern Solution scheme Cyber world Component service offering Deployment Physical world

8 Critical CPS design and challenges 8 Measurement and extraction Parameterization Specification Transformation Completeness consistency Optimization Simulation Design modell Partitioning Verification Complexity? Hardware synthesis Fault modelling, testing Communication synthesis Behavioral model Implementation & testing Scheduling Mobile, ad-hoc, large scale Software synthesis Benchmarking, data processing

9 9 The change in ES design WEB Cyber CPS CLOUD Comp. ES Physical

10 ES paradigm shift Traditional Industrialized Best component technologies Standardized components Automated system design Component integration based system design MDD/Service based integration

11 Service Oriented Approach SOA Embedded systems provide services Information of sensors Internet High level information derived Actuation possibility (limited) Services in a database MDD Upon a new task: solution based on design patterns and available resources New solution deployed no interference with the already running ones

12 Case study: supervising a server room Observations temperature humidity state of doors/windows monitoring the power consumption weather (temp./humidity) temperature of outflow air of air conditioning state of server computers/switches (video based) 12 Fault-Tolerant FIRST Systems Research Group

13 13 The design triangle Weather status forecast Image processing JPEG stream Available via the Internet Threshold Virtualization for sensor drivers CLOUD WEB Cyber CPS ES Sensors Temperature Humidity Web camera Power meter Microswitches windows/doors Comp. Physical

14 ES Physical Budapest University of Technology and Economics Dynamic composition of cyber-physical systems WEB Cyber CPS CLOU D Comp.

15 Composability through abstraction Finding a conceptual domain where devices are homogeneous Possibly the lowest level of such domains Abstraction of computing capabilities Virtualization Abstraction of physical capabilities Sensor virtualization (SOS), Feature discovery Abstraction of communication capabilities Self-describing communication interface (SOS)

16 Sensor Observation Service (SOS) Abstracts sensor data and communication Self-describing sensor information database Stores sensor data with geographic relevance Efficient data queries temporal or spatial filters Members of the CPS direct communication with the SOS procedure 1 offering feature of interest procedure 2

17 W3C Semantic Sensor Network ontology Formal ontology : Capabilities of sensors system, deployment, sensing device, process observed phenomenon (e.g. wind) sensor type (e.g. ultrasonic wind sensor) property (e.g. wind direction) meaning (e.g. blows from direction) unit of measure (e.g. radian) operating range (e.g. temperature, humidity...)

18 Architecture SISRO OWL ontology Application designer User applicatio ns Application monitoring RDF triplestore Configurati on planner Sensor search and monitoring SOS -> OWL transformation Sensor Observation Service install/uninstall start/stop Sensor metadata and observations Node & application performance data Store observation Embedded system Register application Plan execution applications virtualization host OS (Linux) performance riporting Sensor / actuator

19 Core of run-time design 19 Challenge: Detect changes in system state inenvironment Abstractions Design space exploration WEB Cyber Change CPS driven, Incremental MDD CLOUD Comp. ES Physical

20 Dynamic reconfiguration of resources 20 / 16 User applications describe observational and processing constraints System state monitoring Design space exploration Performance monitoring Deployment New system state

21 Incremental Graph Pattern Matching route: Route switchposition sp: SwitchPosition sensor: Sensor routedefinition sensor switch switch: Switch route sp switch sensor r1 sp1 sw1 straight left Main idea: More space to less time Cache matches of patterns Instantly retrieve match (if valid) Update caches upon model changes Notify about relevant changes Approaches: TREAT, LEAPS, RETE, Tools: VIATRA, GROOVE, MoTE, TCore

22 EMF-IncQuery: An Open Source Eclipse Project Declarative graph query language Transitive closure, Negative cond., etc. Compositional, reusable Definition Incremental evaluation Cache result set Maintain incrementally upon model change Execution Derived features, On-the-fly validation View generation, Notifications, Soft links, Databinding, Features

23 IncQUERy-D: Distributed Incremental Model Queries Distributed Model Storage Model Access Adapter Distributed Indexer Distributed Query Evaluator Adaptable to different backend storages Agnostic to graph repres. TripleStores (RDF), EMF, Property graph Surrogate key to identify distibuted elements Graph manip. API Change notifications Type-instance indices, etc. Storedon multiple servers Protects exceeding memory limits Distributed RETE network Distributed termination protocol Constructed and deployed by coordinator node y Decouple and separately distribute Storage, Indexer and Query

24 Design Space Exploration Objectives Global Constraints Design Alternative 1 Design Alternative 2 Initial Design Design Space Exploration Design Alternative 3 Design Alternative 4 Solvers CLP solvers (Choco) model finders (Alloy) meta-heuristics + multi-objective optimization24

25 Self-* properties dynamic challenges and solutions 25 WEB Cyber CPS Selfprotection Selfoptimization Selfconfiguration Selfhealing CLOUD Comp. ES Physical

26 Appearance of cloud-based semantic services Drag-and-drop application prototyping Uniformization Meta-algorithms Data COMPUTED SENSED IBM Internet of Things (IoT) Foundation Wolfram Connected Devices Project 26

27 Self-* properties dynamic challenges Reactiveness Adaptation to the dynamically changing enviroment Mobility Evolution: Problem Requirement Priorities FT Detection Diagnostics Compensation Evolution: Resource set Capability Capacity Workload Selfprotection Selfoptimization Self-healing Selfconfiguration Evolution: Fault/failure modes Evolution: Requirements Threads Effectivity of resource use Load balancing Workload tuning Data protection Detection, Identification, Reaction

28 28 Opportunities and threats in the CPS Aaas algorithm as a service - Meta-algorithms - -Self-checking ABFT - External validator Cheap redundancy Virtualized network (SDN) WEB Cyber CPS Redundancy Multitude of sensors Algorithmic failover/n-version CLOUD Comp. ES Physical

29 Summary Virtualization is a means for Resource sharing Dynamic task allocation Redundancy based fault tolerance Abstraction n-version programming with failover 50 years of FT knowledge has to be revisited MDD Opprtunity for creating self-* systems 29