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CPU Scheduling

Explore CPU scheduling algorithms, resource reservations, and practical applications for efficient and predictable task scheduling in operating systems. Learn about CPU reservations, time constraints, and co-existing multimedia applications.

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CPU Scheduling

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  1. CPU Scheduling

  2. Scheduling Task vs I/O Request • Task computation time unpredictable • Task can be preemptive

  3. CPU Scheduling Algorithm • FCFS • Priority-based • Proportional fair-share • EDF • Rate monotonic

  4. Rate Monotonic

  5. EDF

  6. Problem • How can multimedia applications co-exists with normal applications?

  7. Same Idea as Cello P. Goyal, X. Guo, and H. M. Vin. “A hierarchical CPU scheduler for multimedia operating systems.” OSDI’96

  8. Guarantee? • So far: Best-Effort Real-Time Scheduling • What about: Guaranteed service?

  9. How to Guarantee Services?

  10. Resource Reservation: CPU

  11. Resource Reservations • How to reserve? • Overbook? • Overuse?

  12. Example: Memory • How to reserve? malloc • Overbook? return NULL • Overuse? crash, throw exception

  13. Example: CPU • How to reserve? • Overbook? • Overuse? Memoryis discrete. CPUtimeis continuous.

  14. How to Reserve?

  15. CPU Reservation “I need C units of time .. out of every T units.”

  16. Some Issues • “I need C units of time, out of every T units.”

  17. Effects of T and C

  18. Effects of T and C

  19. Schedulability Theorems • Rate Monotonic Scheduler • Earliest Deadline First

  20. Assumptions • programs are periodic • computation time is constant • zero context switch time • programs are independent

  21. Rialto Scheduler CPU Reservations and Time Contraints: Efficient and Predictable Scheduling of Independent Activities, M. Jones, D. Rosu and M. Rosu

  22. Why Rialto? • Provide CPU reservation • Enable deadlines • Co-exists with best-effort apps

  23. Overview • input: a set of tasks and their reservation • pre-compute a scheduling graph • schedule tasks based on scheduling graph

  24. B E A C B E D F B E A B E D

  25. C A B E F D

  26. Advantages • Only need to make new scheduling decisions when new task is added

  27. Computing Scheduling Graph • Input: A set of tasks with their reservations • Output: A scheduling graph if feasible

  28. Many Possible Solutions C A A B E B E F F D D C

  29. C A F B E F D

  30. Computing Scheduling Graph • Input: A set of tasks with their reservations • Output: A scheduling graph if feasible • Goals: (a) Minimize context switches (b) Distribute free time evenly

  31. Life is Tough.. NP-Hard

  32. Heuristic 10 10 10 10 10 10 10

  33. 10 5 F 5 5 10 10 10

  34. 6 1 F 1 B+E 1 6 6 6

  35. C 4 1 F 1 B+E 1 1 1 A+D 1

  36. Time Constraint • “I need to run the following code, that takes 30ms, 100ms from now, and the deadline is (now+180ms).”

  37. Pseudocode can = begin_constraint(start_time, deadline, estimate) if can schedule then do work else adapt time_taken = end_constraint()

  38. Scheduling Constraints C A F B E F D

  39. Advantages • Only need to make new scheduling decisions when new task is added • Feasibility of time constraint can be predicted accurately

  40. Running Unreserved Task C A F B E F D

  41. Media Player on Rialto/NT

  42. Look inside NT Scheduler • Scheduling unit : Thread • Priority level: 1-15, 16-31 • rt 24 high 13 normal 8 idle 4

  43. Priority Boosting • Example: • Priority +6 after awaken by keyboard input

  44. Anti-Starvation • If hasn’t run for 3 seconds • Boost priority to 14 • Avoid “Priority Inversion”

  45. Mars Pathfinder

  46. C A F B E F D Rialto/NT Rialto Boost Priority to 30 NTScheduler

  47. Win Media Player 6.4

  48. Experiment 1 • WMP running alone

  49. Experiment 2 • WMP running with priority 8 competitor

  50. Experiment 3 • WMP running with priority 10 competitor

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