operating systems cmpsci 377 lecture 6: scheduling

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

Emery BergerUniversity of Massachusetts, Amherst

Operating SystemsCMPSCI 377

Lecture 6: Scheduling

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 2

Last Time: Threads & Scheduling Thread = execution stream within

process User-level, kernel-level, hybrid

No perfect scheduling algorithm Policy decision Goals:

Minimize response time Maximize throughput Fairness


This Time: Scheduling Algorithms FCFS

First-Come, First-Served Round-robin SJF Multilevel Feedback Queues Lottery Scheduling


Round-Robin Scheduling Quantum expires: move to back of

queue Variants used in most real systems

Tradeoffs: length of quantum Large: response time increases

quantum ) 1 = FCFS Small: throughput decreases

quantum ) 0 = overhead dominates context switches, cache misses


Example: Round-Robin

waitingrunning



waitingrunning



waitingrunning

+ Fair- Long

average wait times


Round-Robin vs. FCFS Example 1:

5 jobs, 100 seconds each, quantum = 1s

ignore context switch time



5 jobs, 100 seconds each, quantum = 1s

ignore context switch time



5 jobs: 50, 40, 30, 20, 10 seconds each


Example: SJF

5040

3020

10

Schedule job with least work until I/O or done

0 10 30 60 100 150


Example: SJF

5040

3020

10


0 10 30 60 100 150


Example: SJF

5040

3020

100 10 30 60 100 150



Example: SJF 5 jobs, length 50, 40, 30, 20, 10

seconds


SJF/SRTF: Shortest-Job First Advantages:

Provably optimal – minimizes average waiting time

Works for preemptive & non-preemptive schedulers

Preemptive SJF = SRTF Shortest remaining time first

I/O-bound jobs get priority over CPU-bound jobs

Disadvantages: Impossible to predict CPU time job has left Long-running CPU-bound jobs can starve


Multilevel Feedback Queues (MLFQ) Use past behavior to predict future,

assign job priorities Overcome prediction problem in SJF

Assumption: I/O-bound in past, I/O-bound in future Scheduler favors jobs that used least

CPU time Adaptive:

Change in behavior ) change in scheduling decisions


MLFQ: Approximating SJF Multiple queues, different priorities

Round-robin scheduling at each priority level

Run all at highest priority first, then next, etc.

Can lead to starvation Increase quantum exponentially at

lower priorities


MLFQ Example


MLFQ: Assigning Priorities Job starts in highest priority queue

Quantum expires ) CPU-bound Drop priority one level

Quantum does not expire ) I/O-bound Increase priority one level

CPU-bound jobs move down,I/O-bound jobs move up


Improving Fairness SJF: optimal, but unfair

Increase fairness = give long jobs CPU time degrades average waiting time

Solutions: Each queue – fraction of CPU time

Fair iff even distribution of jobs among queues

Adjust priority of jobs w/o service Originally done by UNIX Avoids starvation Under load, waiting time suffers


Lottery Scheduling Every job gets lottery tickets Each quantum: randomly pick

winner On average:

CPU time proportional to # of tickets Give most tickets to short-running

jobs (approximates SJF) Give every job at least one ticket Degrades gracefully as load changes


Example: Lottery Scheduling

Paying customers: 40%, guests: 60% 2:1 ticket ratio

2 2 1 1 1 1 1 1




2 2 1 1 1 1 1 1


Example: Lottery Scheduling2 2 1 1 1 1 1 1

2/5=40%



Probabilistically achieves desired proportions

2 2 1 1 1 1 1 1

2/5=40%

3/5=60%


Summary of Scheduling Algorithms FCFS:

unfair, average waiting time poor Round robin:

fair, average waiting time poor SJF:

unfair, minimizes average waiting time requires accurate prediction

Multilevel Feedback Queueing: approximates SJF

Lottery scheduling: fair, low average waiting time poor fit to priority


Next Time Synchronization

operating systems cmpsci 377 lecture 6: scheduling

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