Real-Time and Embedded Systems Research Albert M K

Real-Time and Embedded Systems Research Albert M. K. Cheng Real-Time Systems Laboratory Department of Computer Science University of Houston, TX 77204, USA

An Embedded System or Realtime System ¢ ¢ ¢ Real-time system l Produces correct results in a timely manner. Embedded system l computer hardware and software embedded as part of complete device to perform one or a few dedicated functions; often with real-time requirements. Examples: l MMDs, PDAs, Cell phones, GPS, etc.

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-bywire airplane, Drive-by -wire automobile.

Components of a Modern Control and Monitoring System M Monitor/Instruments: Signal processing, Energy conversion User(s)/Operator(s) UI User Interface N D T Decision and Control System: Computer Hardware, Software, Electronics Target System Under Control: Chemical/Fluid, Electrical, Mechanical, Thermal Networking and Communication A Other Components Actuation: Energy conversion, Power modulation

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

Low Power Design for Real. Time Systems ¢ ¢ Low power (energy) consumption is a key design for embedded systems l Battery’s life during operation. l Reliability. l Size of the system. Power-aware real-time scheduling l Minimize the energy consumption • Problem I: Power-aware scheduling for multiple feasible interval jobs. l Achieving some goal while satisfying the real-time and/or energy constraints. • Problem II: Real-time Task Assignment on Rechargeable Multiprocessor System.

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 = CV 2 f -> P = s 3. Slowing down CPU’s speed reduces the energy usage for CPU. Saving energy consumption V. S. Meeting deadline. l Reducing the CPU’s speed as much as possible while meeting every task’s deadline. l A minimum constant speed is always an optimal solution (if possible). l 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 J 1 (1, 9] 2 J 2 (2, 7], (8, 13] 2 J 3 (1, 4], (5, 8], (9, 13] 2

An Example…. Job Feasible Intervals Comp. Time J 1 (1, 9] 2 J 2 (2, 7], (8, 13] 2 J 3 (1, 4], (5, 8], (9, 13] 2

An illustrative example for dynamic fetching Job Feasible Intervals Comp. Time J 1 (1, 9] 2 J 2 (2, 7], (8, 13] 2 J 3 (1, 4], (5, 8], (9, 13] 2 J 4 (4. 5, 8. 5], (9, 14] 3

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: l Pleak = I leak V l P = Pdyn + Pleak l A critical speed s* = s where P(s) = P’(s)s l Shut 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 realtime/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 rechargeable single 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 (Mm. SL), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). While the modifications of the MSL, Mm. SL 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].

Real. Energy: a New Framework and Tool to Evaluate Power-Aware Real-Time Scheduling Algorithms Intel XScale/PXA 255 Module

Example of the Measured Current using Real. Energy

Actual Energy Consumption Using DVS as meaured by Real. Energy

Concluding Remarks ¢ ¢ ¢ ¢ Achieve higher Qo. S in realtime/embedded systems Formal verification New framework for CPS Reduce power consumption Ensure stable power supply Evaluate systems with actual implementations and measurements Deliver actual benefit to society

References ¢ ¢ ¢ J. Lin and A. M. K. Cheng, “Maximizing Guaranteed Qo. S in (m, k)-firm Real-time Systems, ” Proc. 12 th 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. 22 nd 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. 14 th 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. 15 th 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. 2 nd 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 (WCPS 2008), 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. 15 th 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. 15 th 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 ``Real. Energy: 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.