This program, scheduler.py, allows you to see how different schedulers perform under scheduling metrics such as response time, turnaround time, and total wait time. See the README for details.
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Compute the response time and turnaround time when running three jobs of length 200 with the SJF and FIFO schedulers.
$ ./scheduler.py -p SJF -l 200,200,200 -c response time: 0, 200, 400 turnaround time: 200, 400, 600 $ ./scheduler.py -p FIFO -l 200,200,200 -c response time: 0, 200, 400 turnaround time: 200, 400, 600 -
Now do the same but with jobs of different lengths: 100, 200, and 300.
$ ./scheduler.py -p SJF -l 100,200,300 -c response time: 0, 100, 300 turnaround time: 100, 300, 600 $ ./scheduler.py -p FIFO -l 100,200,300 -c response time: 0, 100, 300 turnaround time: 100, 300, 600 -
Now do the same, but also with the RR scheduler and a time-slice of 1.
$ ./scheduler.py -p RR -q 1 -l 100,200,300 -c response time: 0, 1, 2 turnaround time: 298, 499, 600 -
For what types of work loads does SJF deliver the same turnaround times as FIFO?
Jobs are in ascending order by length.
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For what types of workloads and quantum lengths does SJF deliver the same response times as RR?
Jobs length are same and quantum length equates to the job length.
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What happens to response time with SJF as job lengths increase? Can you use the simulator to demonstrate the trend?
Response time will increase.
$ ./scheduler.py -p SJF -l 200,200,200 -c $ ./scheduler.py -p SJF -l 300,300,300 -c $ ./scheduler.py -p SJF -l 400,400,400 -c -
What happens to response time with RR as quantum lengths increase? Can you write an equation that gives the worst-case response time, given N jobs?
1 <= n <= N, q = quantum time, jt = job run time
Response time will increase when q < jt:
The nth job's response time = (n - 1) * q
Average response time = (N - 1) * q / 2When q >= jt:
The nth job's response time = (n - 1) * jt
Average response time = (N - 1) * jt / 2