Real-Time, Embedded, and Cyber-Physical Systems Research Albert M. K. Cheng Professor Real-Time Systems Laboratory Department of Computer Science University.

Real-Time, Embedded, and Cyber-Physical Systems Research

Albert M. K. Cheng

Professor

Real-Time Systems Laboratory

Department of Computer Science

University of Houston, TX 77204, USA

Real-Time Systems Research Group

Director Prof. Albert M. K. Cheng

PhD studentsYong Woon Ahn, Yu Li, Xingliang Zou,

Behnaz Sanati, Sergio Chacon, Zeinab Kazemi, Chaitanya Belwal (just graduated)

MS studentsDaxiao Liu, Yuanfeng Wen (just graduated),

Fang Liu (just graduated)

Undergraduate students (NSF-REU) Mozahid Haque, Kaleb Christoffersen, Dylan Thompson (just completed), James Hyatt (just completed)

Visiting Scholars Yu Jiang, Heilongjiang University, Harbin, China; Qiang Zhou (arriving in November 2013), Beihang University, Beijing, China

Yu Li (Best Junior PhD Student Awardee and Friends of NSM Graduate Fellow) and Prof. Albert Cheng visit the NSF-sponsored Arecibo Observatory (world's largest and most sensitive radiotelescope) in Arecibo, Puerto Rico, after their presentation at the flagship RTSS 2012.

Real-time systems research group at Yuanfeng Wen’s graduation party in May 2013.

An Embedded System or Real-Time System Real-time system

Produces correct results in a timely manner. Embedded system

computer hardware and software embedded as part of a complete device or machine to perform one or more dedicated functions; often with real-time requirements.

Examples: automotive control, avionics, medical systems, autonomous

spacecrafts, industrial process control, mobile devices, and more.

Motivations and Applications: Automotive Control, Avionics, Medical Systems, and Many Embedded Systems

More Applications: Oil Exploration and Production

Control Systems: Old and New Old: Entire control

process is done by mechanical hardware, governed by the mathematics of feedback control.

Examples: Mastered cam grinder, Watt governor, Pneumatic process controller.

New: Advances in electronics and computer systems lead to energetically isolate components of a controlled mechanical system.

Masterless cam grinder, Digital oil production control of pump systems, Fly-by-wire airplane, Drive-by-wire automobile.

Components of a Modern Control and Monitoring System

Target SystemUnder Control:Chemical/Fluid,

Electrical,Mechanical,

Thermal

Decision andControl System:

ComputerHardware,Software,

Electronics

Actuation:Energy conversion,Power modulation

User Interface

Networking andCommunication

Monitor/Instruments:Signal processing,Energy conversion

D T

A

M

UI

N

Other Components

User(s)/Operator(s)

Cyber-Physical System (CPS) Tight conjoining of and coordination between

computational and physical resources. Significantly enhance the adaptability, autonomy,

efficiency, functionality, reliability, safety, and usability of current control systems.

Example: An aerospace CPS will respond more quickly (e.g., automatic aircraft collision avoidance), are more precise (e.g., multiple landings in small airports), work in inaccessible environments (e.g., autonomous space exploration), provide large-scale, distributed coordination (e.g., automated air traffic control), are highly efficient (e.g., long-duration space travel), and augment human capabilities (e.g., tele-robotics).

Correctness of Real-Time Control and Monitoring Systems

Satisfaction of logical correctness constraints

Satisfaction of timing constraints

Design and Implementation Issues Control and monitoring systems: old and

new Model of an embedded/real-time system Scheduling real-time tasks Rate-monotonic scheduler, EDF, LLF Scheduling constraints Multiprocessor scheduling Identical, uniform, heterogeneous

multiprocessors Specification, verification, and debugging

Project 1: Determining Actual Response Time in Functional Reactive Systems

Ascertaining temporal properties is difficult Execution time is dynamic in nature Information known ‘a priori’ cannot be used No notion of Critical Instance

Existing methods for preemptive execution cannot be applied

New methods are required

P-FRP Benefits

Type-safe programming language Discrete and Continuous aspects Transactional model prevents priority

inversion Synchronization primitives not

required

Contribution

This work deals with finding actual response time in P-FRP

Actual time is not an approximate value Actual time is found for a priori known release

scenario Method for finding actual response time is

required for worst-case response time …

… as well as developing exact schedulability tests and analyzing multi-processor schedulability.

Existing Approach: Audsley et al Find response time of

Task j There will be no gaps till

Task j completes Utilization of system till

Task j completes will be 1 No task having lower

priority than Task j will execute

Can be expressed as a Mathematical equality

Existing Approach: Audsley et al

527

21

4

22

627

51

4

52

627

61

4

62

Iteration 1 : 227

01

4

02

Iteration 2 :

Iteration 3 :

Iteration 4 :

Simulation

Execute for each discrete time unit Computational cost dependent on response time Data structures

Queue Time

Overall computational cost is quite high

Gap Enumeration – Storage

•Red-Black Tree

•Self-balancing binary search tree

• log2 n time for insertion, delete and search

Gap Enumeration – Dynamic Size

Iteration 1

Gap Enumeration – Dynamic Size

Iteration 1

Experimental Analysis

7 Tasks

0

2000

4000

6000

8000

10000

0 100 200 300 400 500

Task Set Number

Com

puta

tion

Ste

ps

#Steps-TAS #Steps-GE

Experimental Analysis

Difference vs. Response time - 7 Tasks

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 100 200 300 400

Actual Response Time

Del

ta

Worst-Case Response Time

Combinatorial B-tree for generating release scenarios

Project 1: Conclusions and Ongoing Work New method for response time computation Polynomial-time approach to calculate WCRT Optimal Priority Assignment in P-FRP’s execution

model Static Partitioning Schemes for symmetric multi-

processors Optimizing Energy Use Enhancing Schedulability through reduced

preemptions

Project 2: Real-Time Virtualization

Hierarchical real-time scheduling- Support large-scale systems

- Provide isolation

- Improve resource utilization

Real-time virtual resources- A virtual resource occupies a temporal partition of a physical resource

A Hierarchical Real-time System

Magic7-Pfair-Mixed Algorithm and its Performance of Magic7 with 64 Resources, MaxReg=2

Project 3: Low Power Design for Real-Time Systems Low power (energy) consumption is a key design for

embedded systems Battery’s life during operation. Reliability. Size of the system.

Power-aware real-time scheduling Minimize the energy consumption

• Power-aware scheduling for multiple feasible interval jobs.

Satisfy the real-time constraints. • Real-time Task Assignment on Rechargeable

Multiprocessor System. Reducing energy consumption in portable display devices

Dynamic Voltage Scaling (DVS) Technique for Real-Time Task CPU’s energy/power consumption is a convex function of the

CPU’s speed, e.g. P = CV2f -> P = s3. Slowing down CPU’s speed reduces the energy usage for CPU. Saving energy consumption V.S. Meeting deadline.

Reducing the CPU’s speed as much as possible while meeting every task’s deadline.

A minimum constant speed is always an optimal solution (if possible).

If more than one speed are needed, a “smooth” selection is better.

For regular single instance real-time jobs with only one feasible interval, Yao designed an algorithm for computing the optimal solution.

A Motivational Example (EDF)

Job Feasible Intervals Comp. Time

J1 (1, 9] 2

J2 (2, 7], (8, 13] 2

J3 (1, 4], (5, 8], (9, 13] 2

J2

1 94

0.5

13

J1

0.66

J3

0.4

J2

91

0.5

13

J1

6

J1

(b)

J3

2

(a)

An Example….Job Feasible Intervals Comp. Time

J1 (1, 9] 2

J2 (2, 7], (8, 13] 2

J3 (1, 4], (5, 8], (9, 13] 2

J291

0.5

13J1

6

J1

(a)

J3

2

J2

91

0.5

13

J1

5

J1

(b)

J3

2 4

0.3 0.5

J2

81

0.5

J1

5

J1

(c)

J3

2 4

0.3 0.66

An illustrative example for dynamic fetching

Job Feasible Intervals Comp. Time

J1 (1, 9] 2

J2 (2, 7], (8, 13] 2

J3 (1, 4], (5, 8], (9, 13] 2

J4 (4.5, 8.5], (9, 14] 3

6

J2

1 9 112

0.5

14

J1 J1

1.0

J3 J4

8J2

1 92

0.5

14J1 J1

0.6

J3 J4

0.3

0.66

J2

1 9 112

0.5

14

J1 J1

1.0

J3 J4

0.3

5

5

(a)

(b)

(c)

Considering power consumption for leakage current As VLSI technology marches towards deep

submicron and nanoscale circuits operating at multi-GHz frequencies, the rapidly elevated leakage power dissipation will soon become comparable to, if not exceeding, the dynamic power consumption:Pleak = I leak V

P = Pdyn + Pleak

A critical speed s* = s where P(s) = P’(s)sShut down the CPU when it is idle.

• Shut-down overhead.

Real-time Task Assignment in

Rechargeable Multiprocessor Systems Scheduling of frame-based real-time

tasks in partitioning schemes for multiprocessor systems powered by rechargeable batteries.

In frame-based real-time systems, a set of tasks must execute in a frame, and the whole frame is repeated. This system model is widely used in real-time communication, real-time imaging and a lot of other real-time/embedded systems, including medical systems.

The problem for uniprocessor system has been studied in [Allavena and Mosse 2001], in which an algorithm of complexity O(N) was proposed for determining the feasibility of a task set.

However, doing so in a rechargeable multiprocessor system is NP-Hard [Lin and Cheng 2008].

We propose heuristic and approximation algorithms. Simulation results have shown that our algorithms exhibit very good behavior.

Figure: Algorithm for rechargeablesingle processor [Allavena and Mosse 2001]

Real-time Task Assignment in Heterogeneous Distributed Systems with Rechargeable Batteries

Our techniques to solve the problem are based on four heuristics, namely Minimum Schedule Length (MSL), Min-min Schedule Length (MmSL), Genetic Algorithm (GA), and Ant Colony Optimization (ACO).

While the modifications of the MSL, MmSL and GA approaches from their original implementation are somewhat straight-forward, we design a novel structure using ACO.

Performance comparisons of these four techniques are performed and the results are discussed in [Lin and Cheng 2009].

RealEnergy: a New Framework and Tool to Evaluate Power-Aware Real-Time Scheduling Algorithms

Linux

Measure EnergyTarget System

ApplicationsBenchmarks

RTAI

Data Analysis

Low Power Scheduling

Alg.

Change Frequency

CPU Sleep & Wake-up

Intel XScale/PXA255 Module

Example of the Measured Current

using RealEnergy

Actual Energy Consumption Using DVS as meaured by RealEnergy

Concluding Remarks

Achieve higher QoS in real-time/embedded systems

Formal verification Scheduling New framework for CPS Timing analysis of functional programs Energy/Thermal-aware/Green computing Evaluate systems with actual implementations and

measurements Virtualization and Resource Partitioning Wireless, optical, and sensor networks Deliver actual benefit to society

References J. Lin and A. M. K. Cheng, “Maximizing Guaranteed QoS in (m,k)-firm Real-time Systems,” Proc. 12th

IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Sydney, Australia, Aug. 2006.

J. Lin, Y. H. Chen, and A. M. K. Cheng, "On-Line Burst Header Scheduling in Optical Burst Switching Networks,'' Proc. 22nd IEEE International Conference on Advanced Information Networking and Applications (AINA), Okinawa, Japan, 2008.

J. Lin and A. M. K. Cheng, “Real-time Task Assignment in Recharegable Multiprocessor Systems,” Proc. 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Kaohsiung, Taiwan, Aug. 2006.

J. Lin and A. M. K. Cheng, ``Real-time Task Assignment in Heterogeneous Distributed Systems with Rechargeable Batteries,'' IEEE International Conference on Advanced Information Networking and Applications (AINA), Bradford, UK, May 26-29, 2009.

J. Lin and A. M. K. Cheng, “Real-time Task Assignment with Replication on Multiprocessor Platforms," Proc. 15th IEEE International Conference on Parallel and Distributed Systems (ICPADS), Shenzhen, China, Dec. 8-11, 2009.

A. M. K. Cheng. Real-time systems: scheduling, analysis and verification. Wiley-Interscience, 2002. 2nd printing with updates, 2005.

A. M. K. Cheng, ``Applying (m, k)-firm Scheduling to Medical and Medication Systems,'' Workshop on Software and Systems for Medical Devices and Services (SMDS), in conjunction with IEEE-CS Real-Time Systems Symposium,Tucson, Arizona, Dec. 2007.

A. M. K. Cheng, ``Cyber-Physical Medical and Medication Systems,'' First International Workshop on Cyber-Physical Systems (WCPS2008), sponsored by the United States National Science Foundation, Beijing, China, June 20, 2008 (in conjunction with IEEE ICDCS 2008).

J. Ras and A. M. K. Cheng, ``Response Time Analysis for the Abort-and-Restart Event Handlers of the Priority-Based Functional Reactive Programming (P-FRP) Paradigm,'' Proc. 15th IEEE-CS International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Beijing, China, Aug. 2009. Nominated for Best Paper Award.

J. Lin and A. M. K. Cheng, ``Power-aware scheduling for Multiple Feasible Interval Jobs,'' Proc. 15th IEEE-CS International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Beijing, China, Aug. 2009. Nominated for Best Paper Award.

J. Lin, W. Song, A. M. K. Cheng ``RealEnergy: a New Framework and a Case Study to Evaluate Power-Aware Real-Time Scheduling Algorithms ,'' to appear in ACM International Symposium on Low Power Electronics and Design (ISLPED), Austin, Texas, USA, August 18-20, 2010.

References

Chaitanya Belwal and Albert M. K. Cheng, “Determining Actual Response Time in P-FRP,” Proc. Thirteenth International Symposium on Practical Aspects of Declarative Languages (PADL), Austin, Texas, USA, pages 250-264, January 24-25, 2011.

Chaitanya Belwal and Albert M. K. Cheng, “Determining Actual Response Time in P-FRP using Idle-Period Game Board,” Proc. 14th IEEE International Symposium on Object, Component, and Service-oriented Real-time Distributed Computing (ISORC), Newport Beach, CA, USA, pages 136-143, March 28-31, 2011.

Chaitanya Belwal and Albert M. K. Cheng, “Lazy vs Eager Conflict Detection in Software Transactional Memory: A Real-Time Schedulability Perspective,” IEEE Embedded Systems Letters, Vol. 3, No. 1, March 2011.

Chaitanya Belwal and Albert M. K. Cheng, “Scheduling Conditions for Real-time Software Transactional Memory,” to appear in IEEE Embedded Systems Letters, 2011.

Chaitanya Belwal and Albert M. K. Cheng, “An Extensible Framework for Real-time Task Generation and Simulation,” Proc. 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Toyama, Japan, August 29-31, 2011.

Yuanfeng Wen, Albert M. K. Cheng, and Chaitanya Belwal, ``Worst Case Response Time for Real-Time Software Transactional Memory,'' to appear in ACM Research in Applied Computation Symposium (RACS) Poster Session, San Antonio, Texas, USA, October 23-26, 2012.

Yuanfeng Wen, Chaitanya Belwal, and Albert M. K. Cheng, ``Towards Optimal Priority Assignments for the Transactional Event Handlers of P-FRP,'' to appear in ACM International Conference on Reliable And Convergent Systems (RACS), Montreal, QC, Canada, October 1-4, 2013.

References

Chaitanya Belwal, Albert M. K. Cheng, and Yuanfeng Wen, ``Response Time Bounds for Event Handlers in the Priority based Functional Reactive Programming (P-FRP) Paradigm,'' to appear in ACM Research in Applied Computation Symposium (RACS), San Antonio, Texas, USA, October 23-26, 2012.

Chaitanya Belwal, Albert M. K. Cheng, and Yuanfeng Wen, ``Time Petri Nets for Schedulability Analysis of the Transactional Event Handlers of P-FRP,'' to appear in ACM Research in Applied Computation Symposium (RACS), San Antonio, Texas, USA, October 23-26, 2012.

Yuanfeng Wen, Ziyi Liu, Weidong Shi, Yifei Jiang, Albert M. K. Cheng, Feng Yang, and Abhinav Kohar, ``Support for Power Efficient Mobile Video Playback on Simultaneous Hybrid Display,'' to appear in 10th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia), Tampere, Finland, October 11-12, 2012.

Yuanfeng Wen, Ziyi Liu, Weidong Shi, Yifei Jiang, Albert M. K. Cheng, and Khoa Le, ``Energy Efficient Hybrid Display and Predictive Models for Embedded and Mobile Systems,'' to appear in International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), Tampere, Finland, October 7-12, 2012.

Stefan Andrei, Albert M. K. Cheng, Vlad Radulescu, and Timothy McNicholl, ``Toward an optimal power-aware scheduling technique,'' to appear in 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, September 26-29, 2012.

Weizhe Zhang and Albert M. K. Cheng, ``Performance Prediction of MPI Parallel Jobs in Cluster Environments,'' to appear in International Workshop on Power and QoS Aware Computing (PQoSCom), in conjunction with IEEE Cluster, Beijing, China, September 24-28, 2012.

Albert M. K. Cheng, Homa Niktab, and Michael Walston, ``Timing Analysis of Small Aircraft Transportation System (SATS),'' International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Seoul, Korea, August 2012.

References

Chaitanya Belwal, Albert M. K. Cheng, J. Ras, and Yuanfeng Wen, ``Variable Voltage Scheduling with the Priority-based Functional Reactive Programming Language,'' to appear in ACM International Conference on Reliable And Convergent Systems (RACS), Montreal, QC, Canada, October 1-4, 2013.

Albert M. K. Cheng, Stefan Andrei, and Mozahid Haque, ``Optimizing the Linear Real-Time Logic Verifier,'' 19th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) WIP Session, Philadelphia, PA, April 8, 2013.

Yong woon Ahn and Albert M. K. Cheng, ``Autonomic Computing Architecture for Real-Time Medical Application Running on Virtual Private Cloud Infrastructures,'' 33rd Real-Time Systems Symposium (rtss) WIP Session, San Juan, Puerto Rico, USA, December 4-7, 2012.

Yu Li and Albert M. K. Cheng, `` Static Approximation Algorithms for Regularity-based Resource Partitioning,'' 33rd Real-Time Systems Symposium (RTSS), San Juan, Puerto Rico, USA, December 5-7, 2012.