1 / 15

Discussion: Scheduling

Discussion: Scheduling. Haibo Zeng Amit Mahajan. Outline. Problem: multi-program scheduling on a single processor Optimum fixed priority scheduler – rate monotonic scheduling Optimum dynamic scheduling algorithm – deadline driven scheduling Mixed scheduling algorithm. Introduction.

lorif
Download Presentation

Discussion: Scheduling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Discussion: Scheduling Haibo Zeng Amit Mahajan EE 249, Fall 2002

  2. Outline • Problem: multi-program scheduling on a single processor • Optimum fixed priority scheduler – rate monotonic scheduling • Optimum dynamic scheduling algorithm – deadline driven scheduling • Mixed scheduling algorithm EE 249, Fall 2002

  3. Introduction • Environment: • hard-real-time vs. soft-real-time • Scheduling: • Preemptive and priority driven • Fixed priority vs. dynamic EE 249, Fall 2002

  4. Assumption of environment • A1: The requests for all tasks with hard deadlines are periodic • A2: Task deadline is its next request • A3: The tasks are independent • A4: Run-time for each task is constant • A5: Nonperiodic tasks have no deadlines EE 249, Fall 2002

  5. Outline • Problem: multi-program scheduling on a single processor • Optimum fixed priority scheduler – rate monotonic scheduling • Optimum dynamic scheduling algorithm – deadline driven scheduling • Mixed scheduling algorithm EE 249, Fall 2002

  6. Rate Monotonic Scheduling • According to their request rates only • Higher request rates, higher priorities • Optimum fixed priority scheduling: feasible if any feasible fixed priority assignment exists • Proof: • Use the concept critical instant to analyze the case of scheduling two tasks EE 249, Fall 2002

  7. RMS (cont’d) • Proof: • Use the concept critical instant to analyze the case of scheduling two tasks • Result: assign higher priorities to task with shorter request period; independent of their run-times. • Generalize this result to m tasks EE 249, Fall 2002

  8. RMS (cont’d) • Available Processor Utilization can be as low as: • Analysis: • Right hand side of the inequality is monotonic decreasing with m EE 249, Fall 2002

  9. RMS (cont’d) • Question: • Why utilization factor can’t reach 100%? • Answer: • Processor idle time: example • Question: • How to relax the utilization bound? • Answer: • For i=1,2,…,m-1, • Better choice: dynamic priority assignment EE 249, Fall 2002

  10. Outline • Problem: multi-program scheduling on a single processor • Optimum fixed priority scheduler – rate monotonic scheduling • Optimum dynamic scheduling algorithm deadline driven scheduling • Mixed scheduling algorithm EE 249, Fall 2002

  11. Deadline Driven Scheduling • According to their request rates: earliest deadline first (EDF) • No processor idle time before overflow • Schedulable iff processor use is less than 1 • Optimum scheduling algorithm: feasible if any feasible assignment exists EE 249, Fall 2002

  12. Outline • Problem: multi-program scheduling on a single processor • Optimum fixed priority scheduler – rate monotonic scheduling • Optimum dynamic scheduling algorithm – deadline driven scheduling • Mixed scheduling algorithm EE 249, Fall 2002

  13. Mixed scheduling algorithm • Nice for many applications • Interrupt hardware: fixed priority scheduler • Other software tasks: dynamic priority scheduler • Scheduling algorithm • K tasks of shortest periods: RMS • Remaining slower paced tasks: EDF EE 249, Fall 2002

  14. Mixed scheduling algorithm (cont’d) • Comparison with RMS and EDF: • Still can’t reach 100% utilization • But much better than RMS • Example with 3 tasks • T1=3, T2=4, T3=5 • C1=1, C2=1, C3=1(rate-monotonic), 2(mixed) • RMS: U = 1/3 + 1/4 + 1/5 = 78.3% • Mixed scheduling algorithm: U = 1/3 + 1/4 + 2/5 = 98.3% EE 249, Fall 2002

  15. Questions • Overhead we ignored? • Dynamic scheduling • Preemption • If programs are nonterminating, how about the resources? • Are the assumptions about environment always fine? • If A1 or A4 don’t hold, what do we do? • Is A3 suitable for embedded systems? EE 249, Fall 2002

More Related