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The Internet of Simulations and the agile development of Cyber-physical systems

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Presentation made in the 2017 IEEE System of Systems conference. Co-authored by Stephen Clement, David McKee, Richard Romano and Jie Xu (University of Leeds), Jose-Maria Lopez (Simware Solutions) and David Battersby (Jaguar Land Rover)

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The Internet of Simulation: Enabling Agile Model Based Systems Engineering for Cyber- Physical Systems Stephen Clement, David McKee, Richard Romano, Jie Xu University of Leeds Jose-Maria Lopez-Rodriguez Simware Solutions David Battersby Jaguar Land Rover
Motivation • Large-scale complex cyber-physical systems • 20+ billion IoT devices by 2020 • 300 ExaBytes of data • Increased usage of simulation and virtual engineering • Geographically distributed working with multiple stakeholders • Increasing requirements for rapid prototyping • Increased computing requirement for individual simulations • Growing need for integration and co-simulation to explore system behaviours • Digital twins Raghu Ramakrishnan, CTO for Data “…if we can concentrate and build platforms that will lift the level of abstraction …you could instead focus on understanding whatever your domain is, …[if] the tools for making sense of your data …were that much more accessible …the total cost of ownership would plunge…that's [a] form of [Cloud] amortization. …the tools need to come closer and closer to how you think about your world...” “The way to build a complex system that works is to build it from very simple systems that work.” Kevin Kelly, Founding Executive Editor Wired Magazine
Internet of Simulation • Amortize large-scale simulation and improve co- simulation • Net-Centric simulation platform • Powered by the cloud based SOA • Open APIs to connect simulations, people, processes and things together • Adherence to standards to guarantee connectivity with third-party systems • Simulation as a Service (SIMaaS) • Provides self-contained readily integrated simulations • Hosted as cloud services or on connected devices • Workflow as a Service (WFaaS) • Connect simulations together to define complete systems M&S
SIMaaS: Simulation as a Service • Simulation tools and models are wrapped as services and hosted in the Cloud • Execution, timing and QoS management provided by interfaces and underlying infrastructure • Provides interface for any type of simulation on demand • “Virtual Things” • Uses : • Virtual Engineering • V&V mixing real and virtual • Model of reality or decision support
WFaaS: Workflow as a Service • Combine simulations together by defining interactions and shared data • Hierarchical viewpoint allowing reuse of existing workflows • Provide automated simulation integration via analysis of supplied simulation interfaces and interactions • Combine simulations with data stores and real-world devices • Managed execution of co-simulation: • Scheduling & distributing simulations • Migration of live simulations • Management of QoS A B EA1 D Sub-Workflow
Extending IoS to IoT • Cyber-physical system of systems with virtual components • Virtual design and integration testing • Iterative hardware and software tests • Digital twin, through life-cycle development of cyber-physical systems
Standards: Communication • Data Distribution Service (DDS) • OMG standard for communication • Publish-Subscribe model • Inherent QoS • Web-Services (WS-*) • Collection of supporting standards already widely used in Cloud • Asynchronous Message Queues • Kafka, Rabbit MQ, ØMQ • Common in Software as a Service (SaaS) applications • Supports web standards and protocols
Standards: Composition and Orchestration • Process Workflows • Multiple languages for specification: YAWL, BPMN, BPEL • Defines the flow of execution between services • Many patterns defined by Russell et al. (www.workflowpatterns.com) • Simulation Workflows • Data-flow common in languages such as Simulink and Labview • Inherently parallel, co-simulation is required across workflow • Control time steps and synchronisation of workflow • Mapping between system design and simulations • Simulation interactions imply system interactions
Standards: Integration • Functional Mockup Interface (FMI) • Functional Mockup Units (FMU) as C code packaged with required libraries • Not distributed, single shared memory environment • tool support does not imply full co-simulation. Standards testing only between 2 simulation. • High Level Architecture (HLA) • IEEE 1516-2010 (HLA Evolved) distributed M&S standard • Limited tool support & large barrier to entry • Critical runtime infrastructure only specified as abstract interface Standards define communication, data transfer and synchronisation methods. No modelling framework for widespread simulation integration
Simulation Integration • Complex systems require multi-disciplinary simulation • Heterogeneous development and execution environments • Integration environments • Simulink, SimWorkbench, Modellica, Simware • Social Problems: • Lack of documentation • Lack of centralised repositories • Is my model up to date? • Tool availability? 1. Collect suitable models and documentation 2. Check compatibility of models • Signals • Representations, Internal constants • Time steps 3. Connect models • Connect signals • Transformation functions 4. Validation and Verification • Existing test data • Engineer intuition • Correctness of representation Integration Process
Technical Integration Problems • Differing time steps: requires extrapolation, can result in instantaneous changes • Incompatible constants: causes undefined behaviours, not always immediately recognised • Incompatible signals or representations: Require transformation functions • Lack of API: not easily integrated • Validation and Verification: does the integrated model make sense? • Co-simulation boundaries: especially a problem for physical simulations. Model fidelity Data sharing infrastructure Incompatible interfaces Data types Timing requirements Model complexity Tool incompatibility Engineering Time CoSimulation Infrastructure Model A Model B Independent Development Integration Incompatible Requirements Emergent Properties Negotiation
Agile Systems Engineering using IoS • Given: • Rapid and reliable integration of simulations • Mapping between system design (architecture and interactions) and simulations • Variable fidelity of simulation • Ready connection to physical and virtual components • Allows: • Rapid design iteration • Easy sharing of design state among engineers • Prototype at all stages of development mixing between maturity levels • 3rd party suppliers providing prospective models. • Traverse abstraction layers
Case Studies: Automotive Industry 4.0 Rapid Prototyping 3rd Party Sims Integrate external Systems Integrate with local government models and systems Digital twin factory and supply lines Design offices readily share models and iterate on current designs Multiple update rates and time steps OEM models Data from other vehicles
Case Studies: Driving Sim Throttle Brake Engine Vehicle Dynamics TCU Transmission Micro-Service Service Leeds Driving Simulator
Case Studies: Server Heat • Simulated data-center workload • CPU execution and power draw • Thermodynamic model of heatsink • Fan modelling power and air flow
Case Studies: CITIUS Research new technologies to command, control and interoperate autonomous vehicles in military operations
Conclusion • Growing need for integration of simulation • We already do this, lets do it better! • Internet of Simulation, a possible solution but: • Need increased support from standards and vendors • Design simulations with reuse in mind • Agile Systems Engineering • Example case studies
To know further… University of Leeds - http://institutes.engineering.leeds.ac.uk/computing/research/distsys/ Simware Solutions - http://www.simware.es/internet-of-simulations.html Jaguar Land Rover - http://www.jaguarlandrover.com/2016/technology-lab

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The Internet of Simulations and the agile development of Cyber-physical systems

  • 1. The Internet of Simulation: Enabling Agile Model Based Systems Engineering for Cyber- Physical Systems Stephen Clement, David McKee, Richard Romano, Jie Xu University of Leeds Jose-Maria Lopez-Rodriguez Simware Solutions David Battersby Jaguar Land Rover
  • 2. Motivation • Large-scale complex cyber-physical systems • 20+ billion IoT devices by 2020 • 300 ExaBytes of data • Increased usage of simulation and virtual engineering • Geographically distributed working with multiple stakeholders • Increasing requirements for rapid prototyping • Increased computing requirement for individual simulations • Growing need for integration and co-simulation to explore system behaviours • Digital twins Raghu Ramakrishnan, CTO for Data “…if we can concentrate and build platforms that will lift the level of abstraction …you could instead focus on understanding whatever your domain is, …[if] the tools for making sense of your data …were that much more accessible …the total cost of ownership would plunge…that's [a] form of [Cloud] amortization. …the tools need to come closer and closer to how you think about your world...” “The way to build a complex system that works is to build it from very simple systems that work.” Kevin Kelly, Founding Executive Editor Wired Magazine
  • 3. Internet of Simulation • Amortize large-scale simulation and improve co- simulation • Net-Centric simulation platform • Powered by the cloud based SOA • Open APIs to connect simulations, people, processes and things together • Adherence to standards to guarantee connectivity with third-party systems • Simulation as a Service (SIMaaS) • Provides self-contained readily integrated simulations • Hosted as cloud services or on connected devices • Workflow as a Service (WFaaS) • Connect simulations together to define complete systems M&S
  • 4. SIMaaS: Simulation as a Service • Simulation tools and models are wrapped as services and hosted in the Cloud • Execution, timing and QoS management provided by interfaces and underlying infrastructure • Provides interface for any type of simulation on demand • “Virtual Things” • Uses : • Virtual Engineering • V&V mixing real and virtual • Model of reality or decision support
  • 5. WFaaS: Workflow as a Service • Combine simulations together by defining interactions and shared data • Hierarchical viewpoint allowing reuse of existing workflows • Provide automated simulation integration via analysis of supplied simulation interfaces and interactions • Combine simulations with data stores and real-world devices • Managed execution of co-simulation: • Scheduling & distributing simulations • Migration of live simulations • Management of QoS A B EA1 D Sub-Workflow
  • 6. Extending IoS to IoT • Cyber-physical system of systems with virtual components • Virtual design and integration testing • Iterative hardware and software tests • Digital twin, through life-cycle development of cyber-physical systems
  • 7. Standards: Communication • Data Distribution Service (DDS) • OMG standard for communication • Publish-Subscribe model • Inherent QoS • Web-Services (WS-*) • Collection of supporting standards already widely used in Cloud • Asynchronous Message Queues • Kafka, Rabbit MQ, ØMQ • Common in Software as a Service (SaaS) applications • Supports web standards and protocols
  • 8. Standards: Composition and Orchestration • Process Workflows • Multiple languages for specification: YAWL, BPMN, BPEL • Defines the flow of execution between services • Many patterns defined by Russell et al. (www.workflowpatterns.com) • Simulation Workflows • Data-flow common in languages such as Simulink and Labview • Inherently parallel, co-simulation is required across workflow • Control time steps and synchronisation of workflow • Mapping between system design and simulations • Simulation interactions imply system interactions
  • 9. Standards: Integration • Functional Mockup Interface (FMI) • Functional Mockup Units (FMU) as C code packaged with required libraries • Not distributed, single shared memory environment • tool support does not imply full co-simulation. Standards testing only between 2 simulation. • High Level Architecture (HLA) • IEEE 1516-2010 (HLA Evolved) distributed M&S standard • Limited tool support & large barrier to entry • Critical runtime infrastructure only specified as abstract interface Standards define communication, data transfer and synchronisation methods. No modelling framework for widespread simulation integration
  • 10. Simulation Integration • Complex systems require multi-disciplinary simulation • Heterogeneous development and execution environments • Integration environments • Simulink, SimWorkbench, Modellica, Simware • Social Problems: • Lack of documentation • Lack of centralised repositories • Is my model up to date? • Tool availability? 1. Collect suitable models and documentation 2. Check compatibility of models • Signals • Representations, Internal constants • Time steps 3. Connect models • Connect signals • Transformation functions 4. Validation and Verification • Existing test data • Engineer intuition • Correctness of representation Integration Process
  • 11. Technical Integration Problems • Differing time steps: requires extrapolation, can result in instantaneous changes • Incompatible constants: causes undefined behaviours, not always immediately recognised • Incompatible signals or representations: Require transformation functions • Lack of API: not easily integrated • Validation and Verification: does the integrated model make sense? • Co-simulation boundaries: especially a problem for physical simulations. Model fidelity Data sharing infrastructure Incompatible interfaces Data types Timing requirements Model complexity Tool incompatibility Engineering Time CoSimulation Infrastructure Model A Model B Independent Development Integration Incompatible Requirements Emergent Properties Negotiation
  • 12. Agile Systems Engineering using IoS • Given: • Rapid and reliable integration of simulations • Mapping between system design (architecture and interactions) and simulations • Variable fidelity of simulation • Ready connection to physical and virtual components • Allows: • Rapid design iteration • Easy sharing of design state among engineers • Prototype at all stages of development mixing between maturity levels • 3rd party suppliers providing prospective models. • Traverse abstraction layers
  • 13. Case Studies: Automotive Industry 4.0 Rapid Prototyping 3rd Party Sims Integrate external Systems Integrate with local government models and systems Digital twin factory and supply lines Design offices readily share models and iterate on current designs Multiple update rates and time steps OEM models Data from other vehicles
  • 14. Case Studies: Driving Sim Throttle Brake Engine Vehicle Dynamics TCU Transmission Micro-Service Service Leeds Driving Simulator
  • 15. Case Studies: Server Heat • Simulated data-center workload • CPU execution and power draw • Thermodynamic model of heatsink • Fan modelling power and air flow
  • 16. Case Studies: CITIUS Research new technologies to command, control and interoperate autonomous vehicles in military operations
  • 17. Conclusion • Growing need for integration of simulation • We already do this, lets do it better! • Internet of Simulation, a possible solution but: • Need increased support from standards and vendors • Design simulations with reuse in mind • Agile Systems Engineering • Example case studies
  • 18. To know further… University of Leeds - http://institutes.engineering.leeds.ac.uk/computing/research/distsys/ Simware Solutions - http://www.simware.es/internet-of-simulations.html Jaguar Land Rover - http://www.jaguarlandrover.com/2016/technology-lab