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) T3 T2 T1 RM
EDF
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. Priority inversion J1 J2 J3 = access of single-unit resource R 2 4 6 82 4 6 8 10 12 14 16 18
= access of single-unit resource R

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 2 4 6 8 10 12 14 16 18

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 2 4 6 8 10 12 14 16 18

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 2 4 6 8 10 12 14 16 18

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 T3 T2 T1 2 2 1 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! 2 1 4 8 12
T1 Remaining Execution Actual and Ideal
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. T3 T2 T1 T3 T4 T5 T2 T1 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