Control & Computing in Embedded Systems

Moonju Park

Challenges: Embedded Systems Design

• Time to market puts pressure on design time• The increased complexity (# of components/lines of code,

hetereogeneity, distributed/networked) demands in-creased 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.

Increasing cost of quality and declining product prices

12.5%

8.2%

10.4%

3.0%

5.0%

9.1%

4.3%

2.6%

0%

5%

10%

15%

2003 2004 2005 2006

Mp3PlasmaDVDOther CE

73.9%

6.2%

15.2%1.5%

3.2%

100%

0%

20%

40%

60%

80%

100%

Revenue COGS R&D Other SGA CoQ Profit

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

Cost of Quality (CoQ) as Percent of Revenue

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 manufacturersSource: IBM Analysis

Consumer Electronics Example

Service Industrialization of Manufacturing

• Manufacturing to services shift– IT leads the change

Value creation

R&DManufacturing

Service

Environment is changing: Networked Embedded Systems

• Embedded systems are becoming increasingly networked– Controller-area-networks

(CAN) bus in automobiles– Services in large build-

ings are now run across networks

• e.g. heating, lighting, security

So, what service?

ANOTHER BEERPLEASE HAL…

I’M SORRY DAVE, ICAN’T DO THAT. THEBATHROOM SCALES

AND THE HALL MIRRORARE REPORTING

DISTURBING FLAB ANOMALIES

Cyber-Physical System (CPS)

Definition: Integrations of computation and physical pro-cesses

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 per-

spective)

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.

A CPS Example: Electric power grid

• Current– Equipment protec-

tion devices trip lo-cally

– Cascading failure• Future?

– Real-time coopera-tive control of pro-tection devices

– Self-healing

Another view from Another perspective:A DDDAS Model

(Dynamic, Data-Driven Application Systems)

S p e c t r u m of P h y s i c a l S y s t e m s

Humans3 Hz.

Cosmological:10e-20 Hz.

Subatomic:10e+20 Hz.

ComputationalInfrastructure(grids, perhaps?)

Models

Computations

Discover, Ingest, Interact

Discover,Ingest,Interact

sensors & actuators s & a

A DDDAS Example: Forest Fires

Kirk Complex Fire. U.S.F.S. photo

FireFighters

Policy,Planning,Response

AtmosphericModel

Fire Prop.Model

CombustionModel

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

Technical Challenge• (How) can we build systems that interface be-

tween 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.

Fundamental Scientific Chal-lenges

• Co-existence of Booleans and Re-als– Discrete systems in a continuous

world• Reasoning about uncertainty

– Human, Nature, …

• Understanding complex systems– Emergent behavior, tipping points, …– Chaos theory, randomness, ...

Needs• Services in heterogeneous environ-

ment– Adoption of open standards– Use of web services

• Real-time & Reactive– Not only in embedded systems, but also

in servers

Reactive real-time system• Reactive

– Consisting of many tasks which are exe-cuted in reaction to some external events, or to some other tasks

• Real-time– Tasks must implement the correct func-

tionality, and be executed in a timely manner

Example of reactive real-time sys-tems

• Signal processing– Digital signal processing application for

multimedia (dataflow system)

Conversion from CD audio to DAT audioCD 44.1KHz 88.2KHz 117.6KHzDAT 48KHz

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

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

Feedback control system• Applying control theory to scheduling

e.g. PID control

Feedback Controlled EDF

Problem: Only applicable to control relative delay

Control of dynamic system

• 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

Utilization bound for non-periodic tasks:

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 sys-tem, etc

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

Physical network reconfigura-tion

• Partition of network due to failure of sensor nodes

Unreachable nodes

Physical network reconfigura-tion

• Use mobile agents to restore the communication

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,…