2. Binghamton University EngiNet™ State University of New York

3. Thomas J. Watson School of Engineering and Applied Science

4. WARNING All rights reserved
WARNING All rights reserved. No part of the course materials used in the instruction of this course may be reproduced in any form or by any electronic or mechanical means, including the use of information storage and retrieval systems, without written approval from the copyright owner. ©2009 Binghamton University State University of New York

5. CS 554 Introduction to Real-Time Embedded Systems Professor Kyoung-Don Kang Lecture 1 January 29, 2009

6. Misconceptions About Real-time Computing : A Serious Problem for Next-generation Systems
J. A. Stankovic, Misconceptions about Real-Time Computing: A Serious Problem for Next Generation Systems, IEEE Computer, 21(10), pp , October 1988.

7. Real-Time Computing The correctness of the system
Logical result of the computation
Functional correctness
Time to produce the result
Next generation RT system
Distributed/adaptive
Online guarantees
Long lifetime
Real-time computing

8. There is no science in RT system design
Most good science grew out of attempts to solve practical problems
Real-time system engineers need help
The first flight of a space shuttle was delayed due to a subtle timing bug due to a transient overload during system initialization
A scientific framework to prevent such a bug to be included is needed
Real-time scheduling, resource management, RT programming language, …
Real-time computing

9. Advances in supercomputer hardware will cover RT requirements
One can exploit parallel processors to improve throughput
It does not mean timing constraints can be met automatically
Unless HW is designed to perfectly match the application, the processors and their communication subsystems may not be able to handle all of the task load and time-critical traffic
Real-time task and communication scheduling can get harder as more hardware is used
Real-time computing

10. Demand for computational power often exceeds supply
Intelligent management of finite resources is required
Real-time computing

11. RT Computing = Fast Computing
The objective of fast computing is to minimize the average response time
The objective of real-time computing is to meet the individual timing requirement of each task
Meet individual task deadlines
Fast computing cannot necessarily provide predictability
Fastest hardware & software used in the space shuttle failed to support the timing requirements
Testing is not the answer
Real-time computing

12. Worst case, not average case, response time matters
Fast is relative
Worst case, not average case, response time matters
Not speed but predictability is the goal
Functional and timing behaviors should be as deterministic as necessary to satisfy system specification
Guarantee the delay is shorter than the upper bound
Predictability is not only hardware or algorithmic issue
The delay statement in Ada only specifies the minimum delay before the next task is scheduled
Many things, including scheduling theory, software design, formal methods, RTOS, can change things
Real-time computing

13. Hand-coding may have bugs, especially large RT program
RT Programming = Assembly Coding, Priority Interrupt Programming, Device Driver Writing
Hand-coding may have bugs, especially large RT program
Objective in RT research
Automate
Customized resource scheduler from timing-constraint spec.
Real-time computing

14. RT System Research = Performance Engineering
Common misconceptions(5)
RT System Research = Performance Engineering
To investigate effective resource allocation strategies to satisfy timing-behavior requirement (≈Perf. Engineering)
Specification & verification of timing behavior
Programming language semantics
Theoretical problems
Real-time computing

15. Real-time systems function in static environment
Not necessarily true
Once deployed, real-time systems stay for more than 10 years
Many embedded real-time systems these days need configurable, composable RTOS
Real-time computing

16. Main Research Issues Specification and verification
Modeling and verification of systems that are subject to timing constraints
RT scheduling theory
Meet the specified timing requirements
Support the utilization bound to meet all deadlines
Meet as many deadlines as possible, if it is impossible to meet all deadlines
Real-time computing

17. Main Research Issues RTOS
Guarantee RT constraint
Support FT and distribution
Scheduling time-constrained resource allocation
RT programming language and design methodology
High level abstraction to deal with complex real-time systems
Management of time
Schedulability check
Reusable RT software module
Real-time computing

18. Main Research Issues (Distributed) RTDB Fault tolerance
Concurrency in transaction processing
RT scheduling algorithm
Fault tolerance
Formal specification of the timing constraints
Error handling
RT system architecture
Interconnection topology (process, I/O)
Fast, reliable, and time-constrained communication
Cost-effective and integrated fashion
WCET analysis
Real-time computing

19. Main Research Issues End-to-end deadlines Packet scheduling
RT communication
End-to-end deadlines
Packet scheduling
Dynamic routing
Network buffer management
Wireless Sensor Networks
Newly emerging area
Small, inexpensive, wireless sensors for RT sensing & control
Real-time computing

20. Rate Monotonic, EDF (Earliest Deadline First), and Deadline Monotonic Scheduling Algorithms
C. Liu and J. Layland, Scheduling Algorithm for Multiprogramming in a Hard Real-time Environment, Journal of the ACM, 20(1), pp , January 1973.
N.C. Audsley, A. Burns, M.F. Richardson, and A. J. Wellings, Hard real-time scheduling: The deadline monotonic approach, IEEE Workshop on Real-Time Operating Systems, 1992.
N.C. Audsley, A. Burns, M.F. Richardson, and A. J. Wellings, Applying new scheduling theory to static priority preemptive scheduling, Software Engineering Journal, 8(5): , Sept 1993.
Real-time computing

21. Terminologies Job Task Release time Deadline
Each unit of work that is scheduled and executed by the system
Task
A set of related jobs
For example, a periodic task Ti consists of jobs J1, J2, J3, … coming at every period
Release time
Time instant at which a job becomes available for execution
It can be executed at any time at or after the release time
Deadline
Time instant by which a job should be finished
Relative deadline: Maximum allowable response time
Absolute deadline = release time + relative deadline
Real-time computing

22. Periodic task Ti Job Jik Deadline miss if Period Pi
Worst case execution time Ci
Relative deadline Di
Job Jik
Absolute deadline = release time + relative deadline
Response time = finish time – release time
Deadline miss if
Finish time > absolute deadline
Response time of Jik > Di
Real-time computing

23. Optimal Scheduling Algorithm
A scheduling algorithm S is optimal if S cannot schedule a real-time task set T, no other scheduling algorithm can schedule T
E.g., Rate Monotonic & EDF
Real-time computing

24. Assumptions Single processor Every task is periodic Deadline = period
Tasks are independent
WCET of each task is known
Zero context switch time
Real-time computing

25. Fixed Priority vs. Dynamic Priority Scheduling Algorithms
Fixed priority system
Assign the same priority to all the jobs in each task
Rate monotonic
Dynamic priority system
Assign different priorities to the individual jobs in each task
EDF
Real-time computing

26. Rate Monotonic Optimal fixed priority scheduling algorithm
Shorter period → Higher priority
Higher rate → higher priority
Utilization bound
Real-time computing

27. RM Example t1 t2 t3 time Task Execution Time (C)
End Of Period (T = Period Length)
Real-time computing

28. Utilization Bound (UB) Test
Ui = Ci Ti
Processor Utilization for a task, i
U(n) = n( ) 1 n
Utilization Bound for n tasks
Results:
If U (=S Ui) ≤ U(n) then the set of tasks is schedulable.
If U(n) < S Ui ≤ 1 then the test is inconclusive.
U < U(n) is sufficient but not necessary
Real-time computing

29. Utilization Bound Test
Task
Execution Time (C)
Period (T) t1 40
100 t2
150 t3
350
U1 = 40 / 100 = 0.4
U(3) = 3(21/3 – 1) = 0.779
U2 = 40 / 150 = 0.267
Result:
U1+2 = 0.667, schedulable.
However, < < 1
Therefore, inconclusive for t3.
U3 = 100 / 350 = 0.286
Utotal = 0.953
Real-time computing

30. EDF Shorter absolute deadline → Higher priority
Utilization bound Ub = 1
Ub is necessary and sufficient
Real-time computing

31. Comparisons
RMS
RMS may not guarantee schedulability even when U < 1
Low overhead: Priorities do not change for a fixed task set
EDF
EDF guarantee schedulability as long as U <= 1
High overhead: Task priorities may change dynamically
For more comparisons, refer to “Rate Monotonic vs. EDF: Judgment Day”
Real-time computing

32. Assumptions Single processor Every task is periodic
Relative deadline = period
Tasks are independent
WCET of each task is known
Zero context switch time
What happens if relative deadline < period?
Real-time computing

33. Deadline Monotonic Scheduling Algorithm
Shorter relative deadline → higher priority
RMS is a special case of DMS where Di = Pi
Necessary and sufficient schedulability analysis, called response time analysis, exists
Real-time computing

34. Optimal Scheduling Algorithms Relative Deadline < Period
DMS
Shorter relative deadline → Higher priority
Optimal preemptive fixed priority scheduling
EDF
Shorter absolute deadline → Higher priority
Optimal preemptive dynamic priority scheduling
Real-time computing

35. DMS Response Time Analysis Audsley et al.
Tasks are sorted in non-increasing order of priority. (Ti has the highest priority)
for (each task Ti) {
I = 0; R=0;
while (I + Cj > R)
R = I + Ci;
if (R > Di) return Unschedulable;
I = ∑k=1, i-1 R/Pi Ci; }
return Schedulable
Real-time computing

36. Summary Di = Pi D < P Fixed Priority RMS Utilization bound
Response time analysis
DMS
Dynamic Priority
EDF
Processor demand analysis
Note: EDF is optimal for aperiodic tasks too
Real-time computing

37. Project Ideas
Choice 1: This option actually has two choices. Note that you can start working on this as soon as you learn EDF, RM, and priority inheritance/abort concepts, all of which will be covered within two weeks at most.
(1) Develop a discrete even real-time simulator and implement two out of three programming assignments using the discrete event simulator.
(2) Alternatively, write a well organized object-oriented simulator that allows users to choose all possible combinations of the real-time scheduling options provided by the three programming assignments.
Real-time computing

38. Project Ideas
Choice 2: Develop a video streaming application using Javolution, evaluate performance (i.e., packet losses and delay), and write a report that describes how RTJS and Javolution help writing a real-time application.
Real-time computing

39. Project Ideas
Choice 3: Extend the vehicle merge algorithm developed in Prof. Krithi Ramamritham’s class at IIT. A more detailed description and source code you can extend are available at .
Real-time computing

40. Project Ideas
Choice 4: Extend our Chronos real-time database, for example, to provide more sophisticated/scalable schema/design or data mining/machine learning capability.
Choice 5: Feel free to suggest your own ideas!
Real-time computing