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Welcome!. PhD Dissertation Defense. PhD Candidate: Wenming Li Advisor: Dr. Krishna M. Kavi Committee: Dr. Krishna M. Kavi Dr. Robert Akl Dr. Phil Sweany. Group-EDF - A New Approach And An Efficient Non-Preemptive Algorithm for Soft Real-Time Systems. Contributions.

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  1. Welcome!

  2. PhD Dissertation Defense PhD Candidate: Wenming Li Advisor: Dr. Krishna M. Kavi Committee: Dr. Krishna M. Kavi Dr. Robert Akl Dr. Phil Sweany

  3. Group-EDF - A New Approach And An Efficient Non-Preemptive Algorithm for Soft Real-Time Systems

  4. Contributions • A new approach for soft real-time systems. • A new scheduling algorithm for soft real-time systems and soft Real-Time Operating System (RTOS).

  5. Contributions (Cont’d) • Our research work is a new approach for soft real-time systems. - First proposed the strategy of the dynamic grouping of tasks with deadlines. - First proposed a two-level scheduling scenario for real-time tasks.

  6. Contributions (Cont’d) • Group-EDF is a new scheduling algorithm for soft RTOS and real-time systems. - First proposed to use Earliest Deadline First (EDF) for dynamic groups and Shortest Job First (SJF) within a group.

  7. Focus • Soft real-time systems and soft RTOS. • Non-preemptive scheduling. • Real-time periodic, aperiodic, or sporadic tasks.

  8. The Taxonomy of Real-time Scheduling Our EDF/gEDF algorithm is applicable to the shaded region

  9. Terminology of the Real-Time Model

  10. Hard Real-Time Systems • Every resource management system must work in the correct order to meet time constraints. No deadline miss is allowed. • Disadvantage - Low utilization

  11. Soft Real-Time Systems • It is similar to hard real-time systems. But it is not necessary that every time constraint be met. Some deadline miss is tolerated. • Advantage - High utilization

  12. Non-Preemptive Scheduling • Why non-preemptive? - non-preemptive scheduling is more efficient than preemptive scheduling since preemption incurs context switching overhead which can be significant in fine-grained multithreading systems.

  13. Basic Real-Time Scheduling • First Come First Served (FCFS) • Round Robin (RR) • Shortest Job First (SJF)

  14. First Come First Served (FCFS) • Simple “first in first out” queue • Long average waiting time • Negative for I/O bound processes • Nonpreemptive

  15. Round Robin (RR) • FCFS + preemption with time quantum • Performance (average waiting time) is proportional to the size of the time quantum.

  16. Shortest Job First (SJF) • Optimal with respect to average waiting time. • Requires profiling of the execution times of tasks.

  17. Static Priority Scheduling – Rate-Monotonic (RM) • The shorter the period of a task, the higher is its priority (relative deadline = period). • A set of n independent, periodic jobs can be scheduled by the rate monotonic policy if e1/P1 + e2/P2 + … + en/Pnn (21/n - 1) - The upper bound on utilization is ln2 = 0.69 as n approaches infinity.

  18. Static Priority Scheduling – Deadline-Monotonic (DM) • The shorter the relative deadline of a task, the higher is its priority. • Suitable when relative deadline period • For arbitrary relative deadlines, DM outperforms RM in terms of utilization.

  19. Dynamic Priority Scheduling – Earliest Deadline First (EDF) • The first and the most effectively widely used dynamic priority-driven scheduling algorithm. • Effective for both preemptive and non-preemptive scheduling periodic, aperiodic, and sporadic tasks.

  20. Preemptive EDF • For a set of preemptive periodic, aperiodic, and sporadic tasks, EDF is optimal in the sense that EDF will find a schedule if a schedule is possible for other algorithms. - Approach 100% utilization for periodic tasks

  21. Non-Preemptive EDF • Optimal for sporadic non-preemptive tasks. • Scheduling periodic and aperiodic non-preemptive tasks is NP-hard. - Approach near optimal for non-preemptive scheduling on a uniprocessor system.

  22. Theory of EDF • Minimize maximum lateness Lmax = max {Li | i = 1, …, n} = max {Ci- di | i = 1, …, n} • The problem: 1 | nonpmtn | Lmax • Any sequence of jobs in nondecreasing order of due dates di, results in an optimal schedule. • The scheduling problem {1 | nonpmtn, ri | Lmax}is NP-hard. • Let Lmax = max {Ci- di | i = 1, …, n} = 0, that is, all deadlines of tasks must be met.

  23. POSIX 1003.1b • Portable Operating System Interface (POSIX) 1003.1b, the IEEE Computer Society’s Portable Application Standards Committee (PASC) - SCHED FIFO - SCHED RR - SCHED OTHER

  24. Related Work • Domino Effect of EDF - Overload • Overload Detection And Control - Best-effort by value-density V/C - Admission control - Disadvantage: Needing accurate utilization computing Switching between two scheduling schemes Using Worst Case Execution Time (WCET)

  25. Related Work • SCAN-EDF for disk scheduling - Use SJF to break deadline ties • Quantized deadlines (from CMU) - Static deadline windows

  26. Our Real-time Model • A task (job) in a real-time system or a thread in multithreading processing i is defined as: i = (ri, ei, Di, Pi)

  27. Overview of gEDF • Divide real-time jobs into groups by their deadlines, dynamically. • Groups are based on EDF but tasks within a group may be scheduled based on a different scheme - SJF, Value, Priority, etc. • gEDF is used both in underload and overload.

  28. Overview of gEDF (Cont’d) • We use SJF to enhance EDF, but it is extensible to other scheduling schemes. • gEDF is suitable for non-preemptive soft-real-time systems. • The criteria of selecting suitable grouping policy is flexible • Static deadline windows • Dynamic windows as jobs arrive

  29. Overview of gEDF (Cont’d) • A group in the gEDF algorithm depends on a group range parameter Gr. • A job j belongs to the same group as job i if didj (di + Gr*(di – t)), where t is the current time, 1 i, jN. We group jobs with deadlines that are very close to each other. - The jobs with very close deadlines are in a group (but not necessary if at the boundary of groups)

  30. The gEDF Algorithm • We assume a uniprocessor system. QgEDF is a queue for gEDF scheduling. The current time is represented by t. |QgEDF| represents the length of the queue QgEDF.  = (r, e, D, P) is the job at the head of the queue. - gEDF Group = {k | k QgEDF, dk – d1D1* Gr, 1 km, where m |QgEDF|}, and D1 is the deadline of the first job in a group

  31. The gEDF Algorithm (Cont’d) • Function Enqueue (QgEDF, ) if ( ’s deadline d > t ) then insert job  into QgEDF by Earliest Deadline First, i.e. di di+1di+2, where i, i+1,i+2 QgEDF, 1 i |QgEDF| - 2; end - Enqueue is invoked on job arrivals.

  32. The gEDF Algorithm (Cont’d) • Function Dequeue (QgEDF) if QgEDFthen find a job min with emin = min {ek | k QgEDF, dk – d1Gr*D1, 1 km, where m |QgEDF|}; run it and delete min from QgEDF; end - Dequeue is called when the processor becomes idle.

  33. The Experiment • Used MATLAB provided tools to generate tasks. - In each experiment generated N tasks. - The jobs are scheduling using EDF & gEDF. - The experiment is truncated at a predetermined time T. Success rate is computed based on m out of N jobs completed.

  34. The Experiment (Cont’d) • Varied - Load (or utilization) - Execution time - Deadline (tight deadlines & loose deadlines) - Group range - Deadline tolerance (hard vs. soft real-time)

  35. The Experiment (Cont’d) • For each set of parameters, the experiment is repeated 100 times and the results shown are the averages from the 100 experiments.

  36. Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 0.2

  37. Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 0.5

  38. Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 1.0

  39. Success Ratio: gEDF vs. EDFSummary of the three previous figures

  40. Success Ratio: gEDF vs. EDFSummary of the three previous figures • The gEDF algorithm obtains higher success ratio under higher system loads. • Suitable for soft real-time systems.

  41. Success Ratio: gEDF vs. EDF/Best-Effort/GuaranteeSummary when Tr = 0.5

  42. Effect of Deadline Laxity on Success RatioTight Deadline D = 1 (Deadline = Execution Time)and hard real-time

  43. Effect of Deadline Laxity on Success RatioTight Deadline D = 1 (Deadline = Execution Time)and softer real-time

  44. Effect of Deadline Laxity on Success RatioLoose Deadline D = 5 (Deadline = 5*Execution Time)

  45. Effect of Deadline Laxity on Success RatioLoose Deadline D = 5 (Deadline = 5*Execution Time)

  46. Effect of Deadline on Success RatioSuccess Ratio of EDF when D = 1, 2, 5, 10, and 15(i.e. Deadline = D*Execution Time)

  47. Effect of Deadline onSuccess RatioSuccess Ratio of gEDF when D = 1, 2, 5, 10, and 15(i.e. Deadline = D*Execution Time)

  48. Effect of Deadline onSuccess Ratio • The gEDF algorithm has higher performance (i.e. success ratio) than EDF with greater deadline laxity and greater deadline tolerances.

  49. Effect of Group Range (Gr)Gr = 0.1, 0.2, 0.5, 1.0, Tr = 0.1

  50. Effect of Group Range (Gr)Gr = 0.1, 0.2, 0.5, 1.0, Tr = 0.5

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