1. Department of Computer Science & Engineering
CSCE 790i Final Project Performance of Algorithms for Scheduling Soft Real Time System
Presented by:
Linwei Niu
Department of Computer Science & Engineering
Instructor: Dr. Wang
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November 23, 2018

2. Outline Brief Introduction to Real Time System Scheduling
Schedulability
Scheduling policies
Scheduling algorithms
Performance Comparison by simulation
Conclusion
Question Time
Acknowledgement
In your opening, establish the relevancy of the topic to the audience. Give a brief preview of the presentation and establish value for the listeners. Take into account your audience’s interest and expertise in the topic when choosing your vocabulary, examples, and illustrations. Focus on the importance of the topic to your audience, and you will have more attentive listeners.
November 23, 2018

3. Introduction to Real Time System
Real time means “Real fast “
- the correctness of the computation depends on both the result and the time it consumed.
Hard real time system
- No deadline missing is allowed
Soft real time system
- Occasionally deadline missing is tolerable with some constraint
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November 23, 2018

4. Real time scheduling Given
- a set of tasks (or jobs) with Timing specifications
Arrange
- the running of each task (or job) such that
the timing requirement can be met.
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5. Connections between Real Time Scheduling and Image Processing
Vedio Conference
Remote medical treatment
Online Vedio stream of movies
- each image frame can be considered as a real time job.
- Soft real time systems (occasionally frame missing is tolerable).
- QoS needs to be guaranteed.
Determine the best close for your audience and your presentation. Close with a summary; offer options; recommend a strategy; suggest a plan; set a goal. Keep your focus throughout your presentation, and you will more likely achieve your purpose.
November 23, 2018

6. What does this project focus on ?
Scheduling soft real time system
- occasionally deadline missing is allowed
- Quality of Service needs to be guaranteed.
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7. System Process Model Oi the reaease time of the first job
Given a set of periodic tasks . Each task, say ti is characterized by some parameters
Oi the reaease time of the first job
Ti the period of the task
Di the deadline of each job, Di = Ti
Ci the execution time of the job
mi and ki the QoS constraint of task ti
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8. An Example of Schedule T1=D1=4 C1=1 T2=D2=10 C2=2 T3=D3=15 D3=6 4 8 1216 10 15
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9. Scheduling Policy Priority Driven Fixed Priority strategy Preemptive
- the one with the highest priority is always executed
Fixed Priority strategy
Preemptive
- low priority task can be preempted by high priority task during its running time
Uniprocessor enviorenment
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10. An Example of Schedule T1=D1=4 C1=1 T2=D2=10 C2=2 T3=D3=15 D3=6 4 8 1216 10 15
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11. Quality of Service Constraint
When the processor resource is limited while the workload is high, not all jobs can be completed before its deadline, what to do?
Using QoS constraint
- Out of every k consecutive jobs, we need to guarantee at least m jobs are finished before their deadlines, otherwise the schedule failed.
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12. How to guarantee ? Weakly hard strategy
Two well known existing strategies :
Weakly hard strategy
- all jobs are executed after they arrive , but only those jobs that finished by their deadlines are counted
(m , k) strategy
- partition the jobs into two categories: mandatory ones and optional ones. Mandatory ones always preempts optional ones and are required to finish by their deadlines.
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13. Weakly hard scheduling
Determine the QoS by exact timing analysis
All jobs of the tasks are executed after they arrive until they are finished even if they have missed the deadline.
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14. Exact Timing Analysis
Give a task ti, the k’th instance (called a job) of the task is represented by
- Si(k): the start time of the job;
- Fi(k): the finish time of the job;
- Ri(k): the worst case response time of the job, Ri(k) = Fi(k) - Si(k).
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15. Exact Timing Analysis how to compute Si(k)?
- Si(k) = ( k – 1) Ti + Oi ;
how to compute t = Fi(k)?
- t = kCi + Intfi(t) + idlei(t)
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16. An Example of Schedule T1=D1=4 C1=1 T2=D2=10 C2=2 T3=D3=15 D3=6 4 8 1216 10 15
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17. Exact Timing Analysis
how to compute Intfi(t) ?
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18. Exact Timing Analysis how to compute idlei(t) ?
- using a virtual task to consume the idle time
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19. Exact Timing Analysis get the value of finish time t = Fi(k)
- t = kCi + Intfi(t) + idlei(t)
then we can tell whether the job meet the deadline?
- simply check whether its response time is less than the deadline.
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20. Exact Timing Analysis
use µ-Pattern to represent if the job can meet the deadline
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21. Check The Constraint
After we compute the U-pattern of each job of a task, we got some pattern like this
“ …”
Do this pattern meet the constraint of (2,4)?
How to check ?
- Using a sliding window to check the resulting pattern. If within each window the constrain is met, than we say this task meet the QoS constraint required. Otherwise if within any window it’s not met, then failed.
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22. How many patterns needs to be computed ?
We need to compute all the jobs within the least common multiple(LCM) of all the tasks’ periods.
Some optimization method can be adopted to speed the computation.
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23. Exact Timing Analysis Advantages Disadvantages
- the job execution result can be well predicted offline;
- No simulation is needed.
Disadvantages
- some jobs are still executed until they finished even if it will miss the deadline;
- cannot deal with task sets with very high workloads, e.g. overloaded system.
- How to deal with it ?
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24. (m, k ) Scheduling Mandatory jobs Optional jobs
All jobs are partitioned into two categories:
Mandatory jobs
- Can always preempt the optional jobs, each mandatory job is required to meet the deadline
Optional jobs
- Assigned to the lowest priority, deadline missing is allowed.
November 23, 2018

25. (m, k ) Scheduling How to guarantee the QoS constraint?
- Within any k consecutive jobs, at least m jobs must be partitioned as mandatory jobs. So long as all the mandatory jobs meet the deadline, the constraint is guaranteed.
- Any mandatory job missing the deadline will cause the task schedule to fail.
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26. (m, k ) Scheduling How to achieve this goal?
- let πij represent the pattern of the j’ th job of task ti , then πij is categorized by ( “1” represent mandatory job, “0” represent optional job):
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27. (m, k ) Scheduling Mandatory jobs are evenly distributed
some characteristics of this kind of partition:
Mandatory jobs are evenly distributed
e.g. “ … “ for constraint of (3,6)
The first job are always designated as Mandatory jobs
The mandatory jobs always appear periodicly
- the period of each mandatory job is kiTi
e.g. “ …” for constraint of (2,3)
November 23, 2018

28. (m, k ) Scheduling Advantages
- Only need to guarantee the “mandatory” jobs, so the schedulability of task sets are greatly improved
- Can schedule task sets with very high workloads, even the overloaded systesm.
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29. Problems with this partitioning method:
The first job are always designated as Mandatory jobs
- which makes the task set harder to schedule
Mandatory jobs are evenly distributed
- some time such kind of pattern doesn’t work
while unevenly pattern works better
The patterns are fixed once the constraint given
- which makes it lack of flexibility
November 23, 2018

30. Example of (m,k) schecule1 1 1 1 1
T1=D1=2 C1=2,m1=3,k1=6
T2=D2=6 C2=6,m2=1,k2=2 6 12 18 1 6 12 1 1 1 1 1 1 6 12 18 1 6 12
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31. How to improve it ?
Shift the Mandatory jobs away to make it easier to schedule the low priority jobs
How ?
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32. Shift The Mandatory Jobs Away1 1
T1=D1=6 C1=4,m1=1,k1=2
T2=D2=6 C2=4,m2=1,k2=2 6 12 18 1 1 1 6 12 18 1 1 6 12 18 1 1 6 12 18
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33. Any other problem ?
By shifting the mandatory jobs as far as possible, the task sets become much easier to schedule.
However, the problem of evenly distribution pattern still exists
Can we do better ?
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34. Our New Approach
Using exact timing analysis to choose the pattern for (m, k) schedule
In this way, we combine the advantages of both methods
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35. A Motivation Example T1=D1=2 C1=2,m1=3,k1=6 T2=D2=6 C2=4,m2=1,k2=21 1 1 1
T1=D1=2 C1=2,m1=3,k1=6
T2=D2=6 C2=4,m2=1,k2=2 6 12 18 1 6 12
First decide whether a job can meet the deadline by offline computation. One job will be designated as “mandatory” job only when it can meet the deadline (already know) 1 1 1 1 1 6 12 18 1 6 12
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36. Another Motivation Example1 1 1 1 1
T1=D1=2 C1=2,m1=3,k1=6
T2=D2=6 C2=6,m2=1,k2=2 6 12 18 1 6 12 1 1 1 1 1 1 6 12 18 1 6 12
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37. How to implement
Using (m,k) scheduling pattern as a start point to choose the new pattern
- first we check whether the existing (m,k) pattern of each task is “good” by offline computation. If it is, we keep it and use it to compute the pattern for lower priority tasks;
if not, i.e., it cannot guarantee all mandatory jobs to meet the deadline, then we adjust the pattern with our computation result.
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38. How to implement
Basically, we need to compute with each mandatory job to decide in advance whether it can meet the deadline.
How ?
- We still use the Si(k), Fi(k), Ri(k) in the weakly hard strategy , but we will incorporate the π Pattern in the (m,k) scheudling strategy to it
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39. New Approach how to compute Si(k)? - Si(k) = ( k – 1) Ti + Oi ;
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40. New Approach
how to compute t = Fi(k)?
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41. New Approach
how to compute the Intfi(t)?
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42. New Approach how to compute idlei(t) ?
- using a virtual task to consume the idle time
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43. New Approach How to get the worst case response time of the job ?
Ri(k) = Fi(k) – Si(k)
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44. New Approach
After we got the worst case response time of the job, we can compute the µ-pattern of this job by
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45. New Approach
After we compute the µ-pattern of each job of the current task, we can determine the (m, k) pattern of this job according to it µ-pattern
- basically, we only chose those jobs whose corresponding µ-pattern is “1” to be mandatory jobs.
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46. One example
Consider a task ti with (m,k) constraint of (3,5),supoose the µ-pattern we compute for it is
“ ”,
then no evenly distributed pattern of “10101” will work.
In this case the we can choose the (m,k)-pattern for this task to be
“11001”
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47. New Approach Advantages
- more flexible in choosing (m, k)-pattern for a task set
- schedulability of the task set is greatly improved. (see the experiment results)
- all work can be done offline.
- After we determine the (m,k) pattern of the task sets, we immediately know whether it’s schedulable or not .
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48. Experiment Result
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49. Comparison of Schedulability
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50. Conclusion
Experiment results show that our new approach greatly improved the schedulability of overloaded task sets.
In this project, the performance of two well known soft real time scheduling algorithm are compared. A new approach is then derived by combining the advantages of both algorithms.
Determine the best close for your audience and your presentation. Close with a summary; offer options; recommend a strategy; suggest a plan; set a goal. Keep your focus throughout your presentation, and you will more likely achieve your purpose.
November 23, 2018

51. Thanks! Any Questions ? November 23, 2018
Determine the best close for your audience and your presentation. Close with a summary; offer options; recommend a strategy; suggest a plan; set a goal. Keep your focus throughout your presentation, and you will more likely achieve your purpose.
November 23, 2018

52. Acknowledgements
Thanks to Dr. Wang for the chance to make the presentation
Thanks to all people for listening my presentation
Determine the best close for your audience and your presentation. Close with a summary; offer options; recommend a strategy; suggest a plan; set a goal. Keep your focus throughout your presentation, and you will more likely achieve your purpose.
November 23, 2018