Control & Computing in Embedded Systems

1. Control & Computing in Embedded Systems Moonju Park

2. Challenges: Embedded Systems Design • Time to market puts pressure on design time • The increased complexity (# of components/lines of code, hetereogeneity, distributed/networked) demands increased system design productivity • Quality of new predictable, dependable designs has to improve. • Moving from feasible to optimal systems requires new radical design processes and tools.

3. Increasing cost of quality and declining product prices Consumer Electronics Example Cost of Quality (CoQ) as Percent of Revenue 2006 Company Operating Expense • While CoQ to Sales is increasing for innovative products, those same products are becoming a larger portion of the product mix • Failure to aggressively manage Cost of Quality can lead to a reduction in already-slim profit margins • Innovative products that are fueling growth in CE are often more expensive to fix than traditional products • Rapid price erosion is outpacing reductions in CoQ, resulting in a projected increase in the CoQ/Sales ratio for CE manufacturers Source: IBM Analysis

4. Service Industrialization of Manufacturing Manufacturing to services shift IT leads the change Value creation Manufacturing R&D Service

5. Environment is changing: Networked Embedded Systems Embedded systems are becoming increasingly networked Controller-area-networks (CAN) bus in automobiles Services in large buildings are now run across networks e.g. heating, lighting, security

6. So, what service? ANOTHER BEER PLEASE HAL… I’M SORRY DAVE, I CAN’T DO THAT. THE BATHROOM SCALES AND THE HALL MIRROR ARE REPORTING DISTURBING FLAB ANOMALIES

7. Cyber-Physical System (CPS) • Definition: Integrations of computation and physical processes • Defining characteristics • Cyber capability in every physical component • Networked at multiple & extreme scales • Complex at multiple temporal & spatial scales • Dynamically reorganizing/reconfiguring • High degrees of automation • Unconventional computational & physical substrates • Operation must be dependable • Goals • Integrated physical and cyber design • New science for future engineered systems (10~20 year perspective)

8. Current status ofreal-time systems • Success stories • Spaceships in NASA • Military applications • Application to embedded systems • Voices from outer-community • Unrealistic: model does not fit to many. • Low utilization • Expensive to implement • High-performance will do • We cannot find any real-time application around.

9. A CPS Example: Electric power grid • Current • Equipment protection devices trip locally • Cascading failure • Future? • Real-time cooperative control of protection devices • Self-healing

10. Another view from Another perspective:A DDDAS Model(Dynamic, Data-Driven Application Systems) Discover, Ingest, Interact Models Discover, Ingest, Interact Computations sensors & actuators s & a Computational Infrastructure (grids, perhaps?) Cosmological: 10e-20 Hz. Humans 3 Hz. Subatomic: 10e+20 Hz. S p e c t r u m of P h y s i c a l S y s t e m s

11. Atmospheric Model Fire Prop. Model Combustion Model A DDDAS Example: Forest Fires Policy, Planning, Response Fire Fighters Kirk Complex Fire. U.S.F.S. photo

12. Societal Challenge • How can we provide people and society with cyber-physical systems they can bet their lives on? • Expectations: 24/7 availability, 100% reliability, 100% connectivity, instantaneous response, store anything and everything forever, ... • Classes: young to old, able and disabled, rich and poor, literate and illiterate, … • Numbers: individual  cliques  acquaintances  social networks  cultures  populations Cyber-Physical Systems will be everywhere, used by everyone, for everything

13. Technical Challenge • (How) can we build systems that interface between the cyber world and the physical world? Ideally, with predictable, or at least adaptable behavior. • Why this is hard: • We cannot easily draw the boundaries. • Boundaries are always changing. • There are limits to digitizing the continuous world by abstractions. • Complex systems are unpredictable.

14. Reasoning about uncertainty • Human, Nature, … Fundamental Scientific Challenges • Co-existence of Booleans and Reals • Discrete systems in a continuous world • Understanding complex systems • Emergent behavior, tipping points, … • Chaos theory, randomness, ...

15. Needs • Services in heterogeneous environment • Adoption of open standards • Use of web services • Real-time & Reactive • Not only in embedded systems, but also in servers

16. Reactive real-time system • Reactive • Consisting of many tasks which are executed in reaction to some external events, or to some other tasks • Real-time • Tasks must implement the correct functionality, and be executed in a timely manner

17. Example of reactive real-time systems • Signal processing • Digital signal processing application for multimedia (dataflow system) Conversion from CD audio to DAT audio CD 44.1KHz  88.2KHz  117.6KHz DAT 48KHz

18. Application of control to computing systems • Web-based applications • Web Application Server or HTTP server provides services upon requests from network • Users expect real-time response from server

19. Conventional approach • Generation of static schedule • Problem • High complexity – longer design time • Longer response time • Hard to use in general-purpose computers • Use of periodic task model • Problem • Low utilization due to polling • Complexity in programming due to resource scheduling

20. Feedback control system • Applying control theory to scheduling e.g. PID control

21. Feedback Controlled EDF Problem: Only applicable to control relative delay

22. Control of dynamic system Utilization bound for non-periodic tasks: • Implementation: Apache server on Linux (AMD-based PC), HTTP 1.1 From “Schedulability Analysis and Utilization Bounds for Highly Scalable Real-Time Services” by T.F. Abdelzaher and C. Lu, presented at RTAS 2001

23. Application of computing to control • Networked Control System (NCS) • Feedback control system wherein the control loops are closed through RTN • Aviation system, automotive system, surveillance system, etc

24. Application of computing to control- Example: Control in the Tunnel Scenario Control over sensor network Localization and navigation of mobile robot over sensor network Control of sensor network resources feedback-based adjustment of radio transmit power in sensor network nodes Self-organizing middleware Mobile robot acting as a mobile radio gateway

25. Physical network reconfiguration Partition of network due to failure of sensor nodes Unreachable nodes

26. Physical network reconfiguration Use mobile agents to restore the communication

27. Future Outlook • Extend constituency and application scope • Multidisciplinary integration! • Possible themes: • Computing • Parallelisation & programmability, methodologies and tools, system analysis • System Design • Theory and methods, components and tools for platform-based design • Engineering of Complex, Distributed Systems • Scalability, control, plug & play architectures, large-scale deployment,…