Special Class on Real-Time Systems

1. Special Class on Real-Time Systems

2. Real-time vs. general operating system • In addition to requiring logical correctness, real-time systems require temporal correctness • Logical correctness: Given an input, the system must create the correct output • Temporal correctness: The correct output must be created at the correct time

3. Real-time applications • Real-time systems are used in a variety of applications • Safety critical systems • Airplane autopilot, power plant controllers • Expensive systems • Satellite controllers, Mars rovers • Other time critical applications • Radar system, Sensor networks • Consumer and embedded devices • Cell phones

4. Aspects of real-time research • There are many active areas of research real-time systems • Scheduling algorithms • Schedulability tests • Strategies for reducing power consumption • Real-time operating systems • Real-time programming languages • Specific real-time applications • Hardware for real-time systems

5. Standard real-time system model • Periodic tasks: A mechanism for executing a job repeatedly at regular time intervals • Example: Taking the temperature and initiating appropriate response in a power plant controller • T = (e,p) • e = execution requirement • p = period

6. Example • T = (3,5) • This task generates a new job every 5 time units • Each job will require at most 3 time units to execute • The deadline of each job is equal to the arrival time of the next job

7. Task utilization • Given a periodic task T = (e,p), the utilization of T is e/p • Proportion of processing time this task will require on average • Many schedulability tests are based on task utilization

8. Two common scheduling algorithms • Earliest Deadline First (EDF) • Jobs with earlier deadlines are given higher priority • Rate Monotonic (RM) • Jobs generated by tasks with shorter periods are given higher priority

9. Example RM and EDF schedules Three tasks, T1 = (0.5,3), T2 = (1,4), T3 = (2,6) RM T1 T2 T1 T3 T2 EDF T3 EDF will meet all deadlines if it is possible to do so We say EDF is optimal on uniprocessors

10. Utilization-based EDF test • Given a set of periodic tasks τ= {T1 =(e1,p1), T2=(e2,p2), …, Tn=(en,pn)} • Let U = Σ ei/pi be the total utilization of τ • If U ≤ 1, then τ can be successfully scheduled using preemptive EDF • No jobs will miss their deadlines

11. Utilization-based RM test • Given a set of periodic tasks τ= {T1 =(e1,p1), T2=(e2,p2), …, Tn=(en,pn)} • Let U = Σ ei/pi be the total utilization of τ • If U ≤ ln 2 (≈ 0.69), then τ can be successfully scheduled using preemptive RM • Why use RM? • Many real-time operating systems can only schedule tasks with fixed priority • All jobs generated by the same task must have the same priority

12. Shortcomings of periodic task model • The periodic tasks are assumed to all start at the same time • We can easily add an offset to the task description T = (o,e,p) • Not all tasks can have the job’s deadline equal to the task’s period • We can easily add a deadline to the task description T = (o,e,p,d)

13. More serious shortcomings • The model provided assumes all tasks are independent • Jobs may share resources • In this case, one job may block another job • One job may generate data that will be used by another job • In this case, we would want to impose a precedence constraint on these jobs

14. Scheduling jobs with dependiencies • Both blocking and precedence constraints can cause priority inversion and timing anomolies • Priority inversion: A higher priority job may be forced to wait while a lower priority job executes • Timing anomolies: Reducing the execution of one job may cause another job finish execution at a later time

15. = access of single-unit resource R Priority inversion J1 J2 J3 14 18 16 8 0 6 12 4 10 2

16. Timing anomalies When tasks share resources, there may be timing anomalies. Example:Reducing J3’s critical section from 4 time units to 2.5 causes J1 to miss its deadline! J1 J2 J3 14 18 16 8 0 6 12 4 10 2

17. Multiprocessor Scheduling • Scheduling analysis is much more difficult on multiprocessors • Many tests can only guarantee feasibility when the utilization is approximately m/2, where m is the number of processors • Things get even more complicated when there is resource sharing or precedence constraints

18. Multiprocessor Scheduling • Hong and Leung used the following example to prove that no online scheduling algorithm can be optimal • J1 = J2 = (0; 2; 4); J3 = (0; 4; 8) • Later arrival times as follows • J4 = J5 = (2; 2; 4), and • J4 = J 5 = (4; 4; 8).

19. Example Part 1 • J1 = J2 = (0, 2, 4); J3 = (0, 4, 8) • Later arrival times as follows • J4 = J5 = (2, 2, 4), and • J4‘ = J 5‘ = (4, 4, 8). J3 cannot execute in interval [0,2] J1 J2 J3 J4 J5 14 18 16 8 0 6 12 4 10 2

20. Example Part 2 • J1 = J2 = (0, 2, 4); J3 = (0, 4, 8) • Later arrival times as follows • J4 = J5 = (2, 2, 4), and • J4‘ = J 5‘ = (4, 4, 8). J3 must execute in interval [0,2] J1 J2 J3 J4 J5 14 18 16 8 0 6 12 4 10 2

21. Multiprocessor Scheduling of PTs • There are optimal online algorithms for scheduling periodic tasks on multiprocessors • Pfair, LLREF, DP-Wrap • These tasks make decisions to emulate the ideal schedule

22. Ideal (but impractical) schedule • Ideally, we would execute all tasks at a constant rate • Example T1 = (2,4), T2 = (3,6), and T3 = (6,8) Proportion Processor per Task T1 Remaining Execution 2 2 T1 1 T2 T3 0 4 8 12 • Unfortunately, there are only 2 processors and each can execute only one task at a time!

23. Optimal Algorithms for Multiprocs • All the optimal scheduling algorithms tracks the ideal schedule • Tasks are guaranteed to have executed at ideal rate whenever they have a deadline • Hence, all deadlines are met! T1 Remaining Execution Actual and Ideal 2 1 0 4 8 12 CSCI 8740

24. DP-Fair Scheduling • A global scheduling algorithm for periodic tasks with deadlines equal to periods • The global scheduler executes whenever a job arrives / a deadline occurs • The intervals between scheduling events is called a time slice • At the beginning of each slice of length L, the scheduler allocates uiL units of execution to each task Ti • Every DP-Fair Scheduling algorithm must adhere to 3 “don’t be stupid” rules

25. Don’t Be Stupid Rules • Rule 1: Execute any job with zero laxity • Execute right away if at risk of missing a deadline • Rule 2: Don’t execute any job with zero remaining execution time • Don’t execute if execution time is completed • Rule 3: Do not allow processors to idle for more than L(m – U) time units • Do not idle more than system slack allows

26. DP-Wrap: A Simple DP-Fair Alg • T1=(7,2), T2= (10,3), T3= (9,4), T4 = (12,7), T5 = (14,5) • First time slice is [0,7) • Execution times are 2, 2.1, 3.1, 4.1 and 2.5, resp. T1 T2 T3 T4 T5 T1 T2 T3 T4 T5

27. Extensions to the DP-Fair Rules • DP-Fair can also be used with sporadic tasks and tasks with deadlines not equal to periods • If deadlines are ≥ periods, DP-Fair algorithms are still optimal • If deadlines are < periods optimality no longer holds

28. Other types of real-time systems • Soft real-time systems • Deadlines may be missed • Mixed real-time and non-real-time sytems • Real-time and non-real-time jobs executing in the same system • Want non-real-time jobs to complete as early as possible without causing real-time jobs to miss deadlines

29. Why I like researching real-time systems • Analyzing real-time systems is like solving puzzles • Analysis is visual • Small changes in assumptions can have large impact in analysis