Real-Time Systems, Events, Triggers

Real-Time Systems• A system that has operational deadlines from event

to system response

• A system whose correctness depends on the logical results and the time in which results are produced

• Key Issues– System evolution– Composibility– Software engineering– Performance guarantees– Reliability & formal verification– General system issues– Programming languages– Education

Real World Examples– Intelligent vehicle highway systems– Avionics– Air traffic Control Systems– Multi media– Virtual reality– Defense applications– Nuclear Power Plants– Medical Applications– Process control

Brake Pads

– Dynamically measure the pressure– TactArray Sensors: pressures up to 2000 psi at

temperatures up 200C

Gastrointestinal Diagnostic equipment

• Measures the pressures applied by muscles in the GI tract

FingerTPS

• To teach doctors performing physical manipulations in a consistent and repeatable way

• System records and displays finger and palm pressures exerted during treatment

Soft v. Hard Real-Time Systems

Hard real-time Mission critical systems Catastrophic

consequences

Soft real-time Statistical margin of

error No significant financial

loss

Design Priorities

• Design of HRT is fundamentally different than that of SRT– HRT – temporal domain is as critical as

value domain– SRT – temporal domain is not critical as

value domain

Real-Time Task Models

• Periodic– Continuous & deterministic pattern of

time interval– Characterized as a tuple (C,T)

• e.g., robotics application: sensor data & network transmission

Real-Time Task Models

• Aperiodic– Non-deterministic request periods– Event driven real-time systems– e.g. Ejection of a pilot seat

Technology Trends

• System on a chip– Integrating all components on a

single chip– Cost-effective if mass-produced

Low-Level Design

– Reentrancy• Disable/enable interrupts• EnterCritical / ExitCritical• Semaphores

Example: Blinking LEDv. 1.0 v. 2.0

RT Development Issues– Driving force

• There is an increasing demand for RT embedded systems in various places and novel scenarios

• Safer, cheaper and more reliable• Moving away from old, clunky “legacy systems”

– High level challenges• System evolution• Open real time systems• Composibility• Software engineering

Challenges – System Evolution• System Evolution

– Keeping up with technology trends– System upgrades– Personnel turnover– Vendor changes– Equipment Upgrades– Cost analysis – new system vs. system

upgrade

Challenges – Open RT Systems

– How to create general solution to coexist and support with very specific needs?

– Real-time architecture • Processor speed, caches, memory, buses, and I/O

devises• Multiple applications doing various things (scheduling)

– Perfect execution vs. price• $50 Good enough vs $400 perfect

Challenges – Composability• Function• Time• Fault Tolerance• COTS integration, Web services• Properties at component level must hold at system level• Properties of an ideal component:

– Service provision– Validation– Error containment– Reusability– Design and maintenance

• Principles– Independent development of components– Stability of prior services– Constructive integration

Challenges – Software Engineering

• Need to rapidly develop and deploy large, complex systems

• Software engineering principles

• Processes, methods, tools

• Existing middleware platform do not meet all needs– Evolvability, timing constraints, dependability, etc.

Challenges – Performance

• Science of performance guarantees

• Determining how system will perform under various workloads and still being able to abide by certain requirements (predictability)

• Larger, dynamic systems in various domains and environments

• Determining worst-case scenarios

• Deterministic and probabilistic algorithms

Challenges – V & V

• Simulation, testing, and validation

• Expensive time consuming

• TLYF – Test Like You Fly

• Proving it works

• Meeting various Quality of Service Requirements

• Timing validation

• Scheduling

Verification and Validation• Composable architectures will shift focus back to product

validation• Knowledge about worst-case execution time• Rare event simulations

– Validate fault-tolerance– Peak-load performance

• Formal verification– Critical algorithms