real-time systems, events, triggers. real-time systems a system that has operational deadlines from...
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Real-Time Systems, Events, Triggers
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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
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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
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Brake Pads
– Dynamically measure the pressure– TactArray Sensors: pressures up to 2000 psi at
temperatures up 200C
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Gastrointestinal Diagnostic equipment
• Measures the pressures applied by muscles in the GI tract
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FingerTPS
• To teach doctors performing physical manipulations in a consistent and repeatable way
• System records and displays finger and palm pressures exerted during treatment
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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
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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
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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
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Real-Time Task Models
• Aperiodic– Non-deterministic request periods– Event driven real-time systems– e.g. Ejection of a pilot seat
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Technology Trends
• System on a chip– Integrating all components on a
single chip– Cost-effective if mass-produced
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Low-Level Design
– Reentrancy• Disable/enable interrupts• EnterCritical / ExitCritical• Semaphores
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Example: Blinking LEDv. 1.0 v. 2.0
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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
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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
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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
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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
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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.
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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
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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
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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