1. Networked Embedded Control System - Integration of control and computing Moonju Park Dept. of Computer Science & Engineering University of Incheon 1

2. Introduction  Challenges in embedded systems design Time to market puts pressure on design time The increased complexity (# of components/lines of code, hetereogeneity, distributed/networked) demands increased 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.  Control system constitutes an important subclass of embedded computing systems.

3. Environmental change: Networked Embedded Systems Embedded systems are becoming increasingly networked Controller-area-networks (CAN) bus in automobiles Services in large buildings are now run across networks e.g. heating, lighting, security

4. 4 Controller Plant Node ActuatingSensing Node ActuatingSensing Network Control view of NECS

5.  Due to economic considerations, in spite of the fast development of the computing hardware, embedded systems are resource constrained Limitations on: speed, memory size, communication bandwidth, etc. Use of additional resource (CPU, RAM) is not economically justified. Cost favors general-purpose computing components over specially designed hardware and software.  Quality of control (QoC) not only depends on the control theoretic methods but also efficient management of resources. The key to manage resources is “scheduling” resources. Conventional design of control systems does not consider resource scheduling. Resulting in “implementation-aware control systems.” 5 Control systems in computing system’s perspective

6.  Computing systems (H/W & S/W) are inherently control systems. Computing systems are designed with assumptions, so there are uncertainties in resource utilization that affect the performance. Programs’ execution time Users’ requests Input data Etc. Regarding complex computing systems as controlled dynamics with defined error terms. Use of feedback control method to computing systems can increase the flexibility. Virtually any of computing applications can be considered. 6 Computing systems in control system’s perspective

7.  Given a set of processes to be controlled and a computer with limited computational resource, design a set of controllers and schedule them as real-time tasks such that the overall control performance is optimized. K-E. Årzén & A. Cervin. Control and embedded computing: survey of research directions. Uni-processor case.  Alternative view Design and schedule a set of controllers such that the least expensive implementation platform can be used while still meeting the performance specifications. 7 Co-Design of Control and Scheduling

8. Control of computing systems  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

9. Applying control theory to scheduling  Feedback controlled scheduling system e.g. PID control

10. Feedback Controlled EDF Problem: Only applicable to control relative delay

11.  Web-based applications Web Application Server or HTTP server provides services upon requests from network Users expect real-time response from server 11 Applying control theory to computing systems - Example

12. Control of dynamic computing 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:

13.  A set of digital control loops  Each controller is realized as a separate period task The primary goal of co-design approaches becomes optimizing QoC (Quality of Control) under CPU resource constraints Optimize sampling period, input-output latency subject to performance specification and schedulability. 13 Implementation-aware control - effects of computing system - maximize Quality of control - subject to Schedulability

14.  Sampling frequency affects the system performance High frequency  high control performance, but high network utilization  Less number of controllers  high cost Low frequency  low network utilization and low cost, but low control performance  The upper bound of sampling period Sampling period guaranteeing the stability of the system Stability constraint  The lower bound of sampling period Scheduling period from schedulability constraint 14 Effects of sampling period

15.  Main parts of control loop Data collection Control algorithm computation Output transmission  Timing constraints Sampling period I/O latency (control delay) Though sampling frequency at sensor node is fixed, sampling period at controller may have jitter depending on implementations Scheduling theory can be used to analyze the time variations and delays in control loops. 15 Control loop timing

16.  Three control tasks T1=12ms, T2=8ms, T3=5ms Control loop t=current time LOOP A/D conversion ControlAlgorithmExecution D/A conversion t=t+h WaitUntil(t) END Priorities are given rate-monotonic. Execution time is 2ms. 16 Example

17. Implementation awareness 17 Preemptive scheduling T2 T3 T1 Non-preemptive scheduling T2 T3 T1 I/O latency Sampling period I/O latency Sampling period

18.  Preemptive scheduling Responsiveness Favor high priority tasks (generally) Higher utilization  Non-preemptive scheduling Introduce blocking time (generally) Lower utilization Shorter I/O latency  Control applications’ preference  It is hard to say which one is better 18 Preemptive vs. non-preemptive

19. Application of computing to control

20.  Competing shared network Network bandwidth = m i /h i m i = Tc + Tca + Tsc H i = Transmission period of each control system Tc : Computation time Tca : Controller to actuator transmission time Tsc : Sensor to controller transmission time 20 Networked Control Systems

21. Preemptiveness revisited (short execution time, long network delay) 21 Preemptive scheduling Non-preemptive scheduling TscTcTca TscTcTca (Network is non-preemptive) TscTcTca TscTcTca Error in previous works

22. Preemptiveness revisited (long execution time) 22 Preemptive scheduling Non-preemptive scheduling TscTcTca TscTcTca (Network is non-preemptive) TscTc Tca TscTcTca Higher priority I/O latency

23.  Develop compensation method for jitter Building up control functions for irregular sampling interval Offline calculation + online control 23 Control approach - compensation w/o compensation With compensation

24.  Use of relative deadline different from period  Drawbacks Analysis is complex. There can be utilization loss. 24 Scheduling approach Jitter is reduced

25.  Utilization can be (virtually) arbitrarily small Example: 25 Use of non-preemptive scheduling 010000 T 2  ∞ 10 20000 19991

26.  Dual priority scheme Two fixed priorities are assigned to a task. Priority Threshold: run-time priority Only tasks with priority higher than the threshold of the currently running tasks can preempt.  It can achieve higher utilization than premptive and non-preemptive scheduling  Threshold calculation requires complex calculation – done offline (design time) 26 Thread-X package

27. Quantum-based fixed priority scheduling  Combination of priority-based and quantum-based scheduling Enhances utilization Adopts non-preemptiveness in preemptive scheduling Can be easily implemented on general- purpose computers

28. Quantum-based scheduling vs. Thread-X Achieves higher utilization than Thread-X  Shorter period can be employed

29.  Energy-limited variable voltage microprocessor on which N independent control tasks run concurrently. 29 Reducing power consumption

30.  System Model Nominal execution time C i, sampling period h i is normalized processor speed  Energy Model  Dynamic voltage scaling can be employed to reduce the power consumption. By lowering the voltage, the processor can run slowly. 30 Voltage scaling

31.  Sampling period = 50ms, Computation time = 10ms 31 Idea of DVS 50ms10ms 50ms

32.  Find a balanced engineering solution for a control system. Shorter sampling period  Higher control performance, Higher power consumption Longer sampling period  Poorer control performance, Lower power consumption  Work in progress currently 32 Low-power control

33.  Integration of control and scheduling is an emerging field of research. Application of control theory to computing systems. Design of implementation-aware control systems.  Future research directions Control perspective Event-driven control Dynamic models of computing systems Modeling of embedded control systems Computing perspective Event-driven control Providing temporal determinism of control tasks Supporting tools development Practical implementation 33 Conclusion and Future works