This is mlfq.py

#! /usr/bin/env python

import sys

from optparse import OptionParser

import random

# finds the highest nonempty queue

# -1 if they are all empty

def FindQueue():

q = hiQueue

while q > 0:

if len(queue[q]) > 0:

return q

q -= 1

if len(queue[0]) > 0:

return 0

return -1

def Abort(str):

sys.stderr.write(str + ' ')

exit(1)

#

# PARSE ARGUMENTS

#

parser = OptionParser()

parser.add_option('-s', '--seed', help='the random seed',

default=0, action='store', type='int', dest='seed')

parser.add_option('-n', '--numQueues',

help='number of queues in MLFQ (if not using -Q)',

default=3, action='store', type='int', dest='numQueues')

parser.add_option('-q', '--quantum', help='length of time slice (if not using -Q)',

default=10, action='store', type='int', dest='quantum')

parser.add_option('-a', '--allotment', help='length of allotment (if not using -A)',

default=1, action='store', type='int', dest='allotment')

parser.add_option('-Q', '--quantumList',

help='length of time slice per queue level, specified as ' + \

'x,y,z,... where x is the quantum length for the highest ' + \

'priority queue, y the next highest, and so forth',

default='', action='store', type='string', dest='quantumList')

parser.add_option('-A', '--allotmentList',

help='length of time allotment per queue level, specified as ' + \

'x,y,z,... where x is the # of time slices for the highest ' + \

'priority queue, y the next highest, and so forth',

default='', action='store', type='string', dest='allotmentList')

parser.add_option('-j', '--numJobs', default=3, help='number of jobs in the system',

action='store', type='int', dest='numJobs')

parser.add_option('-m', '--maxlen', default=100, help='max run-time of a job ' +

'(if randomly generating)', action='store', type='int',

dest='maxlen')

parser.add_option('-M', '--maxio', default=10,

help='max I/O frequency of a job (if randomly generating)',

action='store', type='int', dest='maxio')

parser.add_option('-B', '--boost', default=0,

help='how often to boost the priority of all jobs back to ' +

'high priority', action='store', type='int', dest='boost')

parser.add_option('-i', '--iotime', default=5,

help='how long an I/O should last (fixed constant)',

action='store', type='int', dest='ioTime')

parser.add_option('-S', '--stay', default=False,

help='reset and stay at same priority level when issuing I/O',

action='store_true', dest='stay')

parser.add_option('-I', '--iobump', default=False,

help='if specified, jobs that finished I/O move immediately ' + \

'to front of current queue',

action='store_true', dest='iobump')

parser.add_option('-l', '--jlist', default='',

help='a comma-separated list of jobs to run, in the form ' + \

'x1,y1,z1:x2,y2,z2:... where x is start time, y is run ' + \

'time, and z is how often the job issues an I/O request',

action='store', type='string', dest='jlist')

parser.add_option('-c', help='compute answers for me', action='store_true',

default=False, dest='solve')

(options, args) = parser.parse_args()

random.seed(options.seed)

# MLFQ: How Many Queues

numQueues = options.numQueues

quantum = {}

if options.quantumList != '':

# instead, extract number of queues and their time slic

quantumLengths = options.quantumList.split(',')

numQueues = len(quantumLengths)

qc = numQueues - 1

for i in range(numQueues):

quantum[qc] = int(quantumLengths[i])

qc -= 1

else:

for i in range(numQueues):

quantum[i] = int(options.quantum)

allotment = {}

if options.allotmentList != '':

allotmentLengths = options.allotmentList.split(',')

if numQueues != len(allotmentLengths):

print('number of allotments specified must match number of quantums')

exit(1)

qc = numQueues - 1

for i in range(numQueues):

allotment[qc] = int(allotmentLengths[i])

if qc != 0 and allotment[qc]

print('allotment must be positive integer')

exit(1)

qc -= 1

else:

for i in range(numQueues):

allotment[i] = int(options.allotment)

hiQueue = numQueues - 1

# MLFQ: I/O Model

# the time for each IO: not great to have a single fixed time but...

ioTime = int(options.ioTime)

# This tracks when IOs and other interrupts are complete

ioDone = {}

# This stores all info about the jobs

job = {}

# seed the random generator

random.seed(options.seed)

# jlist 'startTime,runTime,ioFreq:startTime,runTime,ioFreq:...'

jobCnt = 0

if options.jlist != '':

allJobs = options.jlist.split(':')

for j in allJobs:

jobInfo = j.split(',')

if len(jobInfo) != 3:

print('Badly formatted job string. Should be x1,y1,z1:x2,y2,z2:...')

print('where x is the startTime, y is the runTime, and z is the I/O frequency.')

exit(1)

assert(len(jobInfo) == 3)

startTime = int(jobInfo[0])

runTime = int(jobInfo[1])

ioFreq = int(jobInfo[2])

job[jobCnt] = {'currPri':hiQueue, 'ticksLeft':quantum[hiQueue],

'allotLeft':allotment[hiQueue], 'startTime':startTime,

'runTime':runTime, 'timeLeft':runTime, 'ioFreq':ioFreq, 'doingIO':False,

'firstRun':-1}

if startTime not in ioDone:

ioDone[startTime] = []

ioDone[startTime].append((jobCnt, 'JOB BEGINS'))

jobCnt += 1

else:

# do something random

for j in range(options.numJobs):

startTime = 0

runTime = int(random.random() * (options.maxlen - 1) + 1)

ioFreq = int(random.random() * (options.maxio - 1) + 1)

job[jobCnt] = {'currPri':hiQueue, 'ticksLeft':quantum[hiQueue],

'allotLeft':allotment[hiQueue], 'startTime':startTime,

'runTime':runTime, 'timeLeft':runTime, 'ioFreq':ioFreq, 'doingIO':False,

'firstRun':-1}

if startTime not in ioDone:

ioDone[startTime] = []

ioDone[startTime].append((jobCnt, 'JOB BEGINS'))

jobCnt += 1

numJobs = len(job)

print 'Here is the list of inputs:'

print 'OPTIONS jobs', numJobs

print 'OPTIONS queues', numQueues

for i in range(len(quantum)-1,-1,-1):

print 'OPTIONS allotments for queue %2d is %3d' % (i, allotment[i])

print 'OPTIONS quantum length for queue %2d is %3d' % (i, quantum[i])

print 'OPTIONS boost', options.boost

print 'OPTIONS ioTime', options.ioTime

print 'OPTIONS stayAfterIO', options.stay

print 'OPTIONS iobump', options.iobump

print ' '

print 'For each job, three defining characteristics are given:'

print ' startTime : at what time does the job enter the system'

print ' runTime : the total CPU time needed by the job to finish'

print ' ioFreq : every ioFreq time units, the job issues an I/O'

print ' (the I/O takes ioTime units to complete) '

print 'Job List:'

for i in range(numJobs):

print ' Job %2d: startTime %3d - runTime %3d - ioFreq %3d' % (i, job[i]['startTime'],

job[i]['runTime'],

job[i]['ioFreq'])

print ''

if options.solve == False:

print 'Compute the execution trace for the given workloads.'

print 'If you would like, also compute the response and turnaround'

print 'times for each of the jobs.'

print ''

print 'Use the -c flag to get the exact results when you are finished. '

exit(0)

# initialize the MLFQ queues

queue = {}

for q in range(numQueues):

queue[q] = []

# TIME IS CENTRAL

currTime = 0

# use these to know when we're finished

totalJobs = len(job)

finishedJobs = 0

print ' Execution Trace: '

while finishedJobs

# find highest priority job

# run it until either

# (a) the job uses up its time quantum

# (b) the job performs an I/O

# check for priority boost

if options.boost > 0 and currTime != 0:

if currTime % options.boost == 0:

print '[ time %d ] BOOST ( every %d )' % (currTime, options.boost)

# remove all jobs from queues (except high queue) and put them in high queue

for q in range(numQueues-1):

for j in queue[q]:

if job[j]['doingIO'] == False:

queue[hiQueue].append(j)

queue[q] = []

# change priority to high priority

# reset number of ticks left for all jobs (just for lower jobs?)

# add to highest run queue (if not doing I/O)

for j in range(numJobs):

# print '-> Boost %d (timeLeft %d)' % (j, job[j]['timeLeft'])

if job[j]['timeLeft'] > 0:

# print '-> FinalBoost %d (timeLeft %d)' % (j, job[j]['timeLeft'])

job[j]['currPri'] = hiQueue

job[j]['ticksLeft'] = allotment[hiQueue]

# print 'BOOST END: QUEUES look like:', queue

# check for any I/Os done

if currTime in ioDone:

for (j, type) in ioDone[currTime]:

q = job[j]['currPri']

job[j]['doingIO'] = False

print '[ time %d ] %s by JOB %d' % (currTime, type, j)

if options.iobump == False or type == 'JOB BEGINS':

queue[q].append(j)

else:

queue[q].insert(0, j)

# now find the highest priority job

currQueue = FindQueue()

if currQueue == -1:

print '[ time %d ] IDLE' % (currTime)

currTime += 1

continue

# there was at least one runnable job, and hence ...

currJob = queue[currQueue][0]

if job[currJob]['currPri'] != currQueue:

Abort('currPri[%d] does not match currQueue[%d]' % (job[currJob]['currPri'], currQueue))

job[currJob]['timeLeft'] -= 1

job[currJob]['ticksLeft'] -= 1

if job[currJob]['firstRun'] == -1:

job[currJob]['firstRun'] = currTime

runTime = job[currJob]['runTime']

ioFreq = job[currJob]['ioFreq']

ticksLeft = job[currJob]['ticksLeft']

allotLeft = job[currJob]['allotLeft']

timeLeft = job[currJob]['timeLeft']

print '[ time %d ] Run JOB %d at PRIORITY %d [ TICKS %d ALLOT %d TIME %d (of %d) ]' % \

(currTime, currJob, currQueue, ticksLeft, allotLeft, timeLeft, runTime)

if timeLeft

Abort('Error: should never have less than 0 time left to run')

# UPDATE TIME

currTime += 1

# CHECK FOR JOB ENDING

if timeLeft == 0:

print '[ time %d ] FINISHED JOB %d' % (currTime, currJob)

finishedJobs += 1

job[currJob]['endTime'] = currTime

# print 'BEFORE POP', queue

done = queue[currQueue].pop(0)

# print 'AFTER POP', queue

assert(done == currJob)

continue

# CHECK FOR IO

issuedIO = False

if ioFreq > 0 and (((runTime - timeLeft) % ioFreq) == 0):

# time for an IO!

print '[ time %d ] IO_START by JOB %d' % (currTime, currJob)

issuedIO = True

desched = queue[currQueue].pop(0)

assert(desched == currJob)

job[currJob]['doingIO'] = True

# this does the bad rule -- reset your tick counter if you stay at the same level

if options.stay == True:

job[currJob]['ticksLeft'] = quantum[currQueue]

job[currJob]['allotLeft'] = allotment[currQueue]

# add to IO Queue: but which queue?

futureTime = currTime + ioTime

if futureTime not in ioDone:

ioDone[futureTime] = []

print 'IO DONE'

ioDone[futureTime].append((currJob, 'IO_DONE'))

# CHECK FOR QUANTUM ENDING AT THIS LEVEL (BUT REMEMBER, THERE STILL MAY BE ALLOTMENT LEFT)

if ticksLeft == 0:

if issuedIO == False:

# IO HAS NOT BEEN ISSUED (therefor pop from queue)'

desched = queue[currQueue].pop(0)

assert(desched == currJob)

job[currJob]['allotLeft'] = job[currJob]['allotLeft'] - 1

if job[currJob]['allotLeft'] == 0:

# this job is DONE at this level, so move on

if currQueue > 0:

# in this case, have to change the priority of the job

job[currJob]['currPri'] = currQueue - 1

job[currJob]['ticksLeft'] = quantum[currQueue-1]

job[currJob]['allotLeft'] = allotment[currQueue-1]

if issuedIO == False:

queue[currQueue-1].append(currJob)

else:

job[currJob]['ticksLeft'] = quantum[currQueue]

job[currJob]['allotLeft'] = allotment[currQueue]

if issuedIO == False:

queue[currQueue].append(currJob)

else:

# this job has more time at this level, so just push it to end

job[currJob]['ticksLeft'] = quantum[currQueue]

if issuedIO == False:

queue[currQueue].append(currJob)

# print out statistics

print ''

print 'Final statistics:'

responseSum = 0

turnaroundSum = 0

for i in range(numJobs):

response = job[i]['firstRun'] - job[i]['startTime']

turnaround = job[i]['endTime'] - job[i]['startTime']

print ' Job %2d: startTime %3d - response %3d - turnaround %3d' % (i, job[i]['startTime'],

response, turnaround)

responseSum += response

turnaroundSum += turnaround

print ' Avg %2d: startTime n/a - response %.2f - turnaround %.2f' % (i,

float(responseSum)umJobs,

float(turnaroundSum)umJobs)

print ' '

can i have the screenshots of the results too while running the each command asked in the questions.

Thank you!!

python 2.7 version.

#! /usr/bin/env python

import sys

from optparse import OptionParser

import random

# finds the highest nonempty queue

# -1 if they are all empty

def FindQueue():

q = hiQueue

while q > 0:

if len(queue[q]) > 0:

return q

q -= 1

if len(queue[0]) > 0:

return 0

return -1

def Abort(str):

sys.stderr.write(str + ' ')

exit(1)

#

# PARSE ARGUMENTS

#

parser = OptionParser()

parser.add_option('-s', '--seed', help='the random seed',

default=0, action='store', type='int', dest='seed')

parser.add_option('-n', '--numQueues',

help='number of queues in MLFQ (if not using -Q)',

default=3, action='store', type='int', dest='numQueues')

parser.add_option('-q', '--quantum', help='length of time slice (if not using -Q)',

default=10, action='store', type='int', dest='quantum')

parser.add_option('-a', '--allotment', help='length of allotment (if not using -A)',

default=1, action='store', type='int', dest='allotment')

parser.add_option('-Q', '--quantumList',

help='length of time slice per queue level, specified as ' + \

'x,y,z,... where x is the quantum length for the highest ' + \

'priority queue, y the next highest, and so forth',

default='', action='store', type='string', dest='quantumList')

parser.add_option('-A', '--allotmentList',

help='length of time allotment per queue level, specified as ' + \

'x,y,z,... where x is the # of time slices for the highest ' + \

'priority queue, y the next highest, and so forth',

default='', action='store', type='string', dest='allotmentList')

parser.add_option('-j', '--numJobs', default=3, help='number of jobs in the system',

action='store', type='int', dest='numJobs')

parser.add_option('-m', '--maxlen', default=100, help='max run-time of a job ' +

'(if randomly generating)', action='store', type='int',

dest='maxlen')

parser.add_option('-M', '--maxio', default=10,

help='max I/O frequency of a job (if randomly generating)',

action='store', type='int', dest='maxio')

parser.add_option('-B', '--boost', default=0,

help='how often to boost the priority of all jobs back to ' +

'high priority', action='store', type='int', dest='boost')

parser.add_option('-i', '--iotime', default=5,

help='how long an I/O should last (fixed constant)',

action='store', type='int', dest='ioTime')

parser.add_option('-S', '--stay', default=False,

help='reset and stay at same priority level when issuing I/O',

action='store_true', dest='stay')

parser.add_option('-I', '--iobump', default=False,

help='if specified, jobs that finished I/O move immediately ' + \

'to front of current queue',

action='store_true', dest='iobump')

parser.add_option('-l', '--jlist', default='',

help='a comma-separated list of jobs to run, in the form ' + \

'x1,y1,z1:x2,y2,z2:... where x is start time, y is run ' + \

'time, and z is how often the job issues an I/O request',

action='store', type='string', dest='jlist')

parser.add_option('-c', help='compute answers for me', action='store_true',

default=False, dest='solve')

(options, args) = parser.parse_args()

random.seed(options.seed)

# MLFQ: How Many Queues

numQueues = options.numQueues

quantum = {}

if options.quantumList != '':

# instead, extract number of queues and their time slic

quantumLengths = options.quantumList.split(',')

numQueues = len(quantumLengths)

qc = numQueues - 1

for i in range(numQueues):

quantum[qc] = int(quantumLengths[i])

qc -= 1

else:

for i in range(numQueues):

quantum[i] = int(options.quantum)

allotment = {}

if options.allotmentList != '':

allotmentLengths = options.allotmentList.split(',')

if numQueues != len(allotmentLengths):

print('number of allotments specified must match number of quantums')

exit(1)

qc = numQueues - 1

for i in range(numQueues):

allotment[qc] = int(allotmentLengths[i])

if qc != 0 and allotment[qc]

print('allotment must be positive integer')

exit(1)

qc -= 1

else:

for i in range(numQueues):

allotment[i] = int(options.allotment)

hiQueue = numQueues - 1

# MLFQ: I/O Model

# the time for each IO: not great to have a single fixed time but...

ioTime = int(options.ioTime)

# This tracks when IOs and other interrupts are complete

ioDone = {}

# This stores all info about the jobs

job = {}

# seed the random generator

random.seed(options.seed)

# jlist 'startTime,runTime,ioFreq:startTime,runTime,ioFreq:...'

jobCnt = 0

if options.jlist != '':

allJobs = options.jlist.split(':')

for j in allJobs:

jobInfo = j.split(',')

if len(jobInfo) != 3:

print('Badly formatted job string. Should be x1,y1,z1:x2,y2,z2:...')

print('where x is the startTime, y is the runTime, and z is the I/O frequency.')

exit(1)

assert(len(jobInfo) == 3)

startTime = int(jobInfo[0])

runTime = int(jobInfo[1])

ioFreq = int(jobInfo[2])

job[jobCnt] = {'currPri':hiQueue, 'ticksLeft':quantum[hiQueue],

'allotLeft':allotment[hiQueue], 'startTime':startTime,

'runTime':runTime, 'timeLeft':runTime, 'ioFreq':ioFreq, 'doingIO':False,

'firstRun':-1}

if startTime not in ioDone:

ioDone[startTime] = []

ioDone[startTime].append((jobCnt, 'JOB BEGINS'))

jobCnt += 1

else:

# do something random

for j in range(options.numJobs):

startTime = 0

runTime = int(random.random() * (options.maxlen - 1) + 1)

ioFreq = int(random.random() * (options.maxio - 1) + 1)

job[jobCnt] = {'currPri':hiQueue, 'ticksLeft':quantum[hiQueue],

'allotLeft':allotment[hiQueue], 'startTime':startTime,

'runTime':runTime, 'timeLeft':runTime, 'ioFreq':ioFreq, 'doingIO':False,

'firstRun':-1}

if startTime not in ioDone:

ioDone[startTime] = []

ioDone[startTime].append((jobCnt, 'JOB BEGINS'))

jobCnt += 1

numJobs = len(job)

print 'Here is the list of inputs:'

print 'OPTIONS jobs', numJobs

print 'OPTIONS queues', numQueues

for i in range(len(quantum)-1,-1,-1):

print 'OPTIONS allotments for queue %2d is %3d' % (i, allotment[i])

print 'OPTIONS quantum length for queue %2d is %3d' % (i, quantum[i])

print 'OPTIONS boost', options.boost

print 'OPTIONS ioTime', options.ioTime

print 'OPTIONS stayAfterIO', options.stay

print 'OPTIONS iobump', options.iobump

print ' '

print 'For each job, three defining characteristics are given:'

print ' startTime : at what time does the job enter the system'

print ' runTime : the total CPU time needed by the job to finish'

print ' ioFreq : every ioFreq time units, the job issues an I/O'

print ' (the I/O takes ioTime units to complete) '

print 'Job List:'

for i in range(numJobs):

print ' Job %2d: startTime %3d - runTime %3d - ioFreq %3d' % (i, job[i]['startTime'],

job[i]['runTime'],

job[i]['ioFreq'])

print ''

if options.solve == False:

print 'Compute the execution trace for the given workloads.'

print 'If you would like, also compute the response and turnaround'

print 'times for each of the jobs.'

print ''

print 'Use the -c flag to get the exact results when you are finished. '

exit(0)

# initialize the MLFQ queues

queue = {}

for q in range(numQueues):

queue[q] = []

# TIME IS CENTRAL

currTime = 0

# use these to know when we're finished

totalJobs = len(job)

finishedJobs = 0

print ' Execution Trace: '

while finishedJobs

# find highest priority job

# run it until either

# (a) the job uses up its time quantum

# (b) the job performs an I/O

# check for priority boost

if options.boost > 0 and currTime != 0:

if currTime % options.boost == 0:

print '[ time %d ] BOOST ( every %d )' % (currTime, options.boost)

# remove all jobs from queues (except high queue) and put them in high queue

for q in range(numQueues-1):

for j in queue[q]:

if job[j]['doingIO'] == False:

queue[hiQueue].append(j)

queue[q] = []

# change priority to high priority

# reset number of ticks left for all jobs (just for lower jobs?)

# add to highest run queue (if not doing I/O)

for j in range(numJobs):

# print '-> Boost %d (timeLeft %d)' % (j, job[j]['timeLeft'])

if job[j]['timeLeft'] > 0:

# print '-> FinalBoost %d (timeLeft %d)' % (j, job[j]['timeLeft'])

job[j]['currPri'] = hiQueue

job[j]['ticksLeft'] = allotment[hiQueue]

# print 'BOOST END: QUEUES look like:', queue

# check for any I/Os done

if currTime in ioDone:

for (j, type) in ioDone[currTime]:

q = job[j]['currPri']

job[j]['doingIO'] = False

print '[ time %d ] %s by JOB %d' % (currTime, type, j)

if options.iobump == False or type == 'JOB BEGINS':

queue[q].append(j)

else:

queue[q].insert(0, j)

# now find the highest priority job

currQueue = FindQueue()

if currQueue == -1:

print '[ time %d ] IDLE' % (currTime)

currTime += 1

continue

# there was at least one runnable job, and hence ...

currJob = queue[currQueue][0]

if job[currJob]['currPri'] != currQueue:

Abort('currPri[%d] does not match currQueue[%d]' % (job[currJob]['currPri'], currQueue))

job[currJob]['timeLeft'] -= 1

job[currJob]['ticksLeft'] -= 1

if job[currJob]['firstRun'] == -1:

job[currJob]['firstRun'] = currTime

runTime = job[currJob]['runTime']

ioFreq = job[currJob]['ioFreq']

ticksLeft = job[currJob]['ticksLeft']

allotLeft = job[currJob]['allotLeft']

timeLeft = job[currJob]['timeLeft']

print '[ time %d ] Run JOB %d at PRIORITY %d [ TICKS %d ALLOT %d TIME %d (of %d) ]' % \

(currTime, currJob, currQueue, ticksLeft, allotLeft, timeLeft, runTime)

if timeLeft

Abort('Error: should never have less than 0 time left to run')

# UPDATE TIME

currTime += 1

# CHECK FOR JOB ENDING

if timeLeft == 0:

print '[ time %d ] FINISHED JOB %d' % (currTime, currJob)

finishedJobs += 1

job[currJob]['endTime'] = currTime

# print 'BEFORE POP', queue

done = queue[currQueue].pop(0)

# print 'AFTER POP', queue

assert(done == currJob)

continue

# CHECK FOR IO

issuedIO = False

if ioFreq > 0 and (((runTime - timeLeft) % ioFreq) == 0):

# time for an IO!

print '[ time %d ] IO_START by JOB %d' % (currTime, currJob)

issuedIO = True

desched = queue[currQueue].pop(0)

assert(desched == currJob)

job[currJob]['doingIO'] = True

# this does the bad rule -- reset your tick counter if you stay at the same level

if options.stay == True:

job[currJob]['ticksLeft'] = quantum[currQueue]

job[currJob]['allotLeft'] = allotment[currQueue]

# add to IO Queue: but which queue?

futureTime = currTime + ioTime

if futureTime not in ioDone:

ioDone[futureTime] = []

print 'IO DONE'

ioDone[futureTime].append((currJob, 'IO_DONE'))

# CHECK FOR QUANTUM ENDING AT THIS LEVEL (BUT REMEMBER, THERE STILL MAY BE ALLOTMENT LEFT)

if ticksLeft == 0:

if issuedIO == False:

# IO HAS NOT BEEN ISSUED (therefor pop from queue)'

desched = queue[currQueue].pop(0)

assert(desched == currJob)

job[currJob]['allotLeft'] = job[currJob]['allotLeft'] - 1

if job[currJob]['allotLeft'] == 0:

# this job is DONE at this level, so move on

if currQueue > 0:

# in this case, have to change the priority of the job

job[currJob]['currPri'] = currQueue - 1

job[currJob]['ticksLeft'] = quantum[currQueue-1]

job[currJob]['allotLeft'] = allotment[currQueue-1]

if issuedIO == False:

queue[currQueue-1].append(currJob)

else:

job[currJob]['ticksLeft'] = quantum[currQueue]

job[currJob]['allotLeft'] = allotment[currQueue]

if issuedIO == False:

queue[currQueue].append(currJob)

else:

# this job has more time at this level, so just push it to end

job[currJob]['ticksLeft'] = quantum[currQueue]

if issuedIO == False:

queue[currQueue].append(currJob)

# print out statistics

print ''

print 'Final statistics:'

responseSum = 0

turnaroundSum = 0

for i in range(numJobs):

response = job[i]['firstRun'] - job[i]['startTime']

turnaround = job[i]['endTime'] - job[i]['startTime']

print ' Job %2d: startTime %3d - response %3d - turnaround %3d' % (i, job[i]['startTime'],

response, turnaround)

responseSum += response

turnaroundSum += turnaround

print ' Avg %2d: startTime n/a - response %.2f - turnaround %.2f' % (i,

float(responseSum)umJobs,

float(turnaroundSum)umJobs)

print ' '

12 SCHEDULING: THE MULTI-LEVEL FEEDBACK QUEUE Homework (Simulation) This program, mlfg.py, allows you to see how the MLFQ scheduler presented in this chapter behaves. See the README for details. Questions 1. Run a few randomly-generated problems with just two jobs and two queues; compute the MLFQ execution trace for each. Make your life easier by limiting the length of each job and turning off /Os. 2. How would you run the scheduler to reproduce each of the exam- ples in the chapter? 3. How would you configure the scheduler parameters to behave just like a round-robin scheduler? 4. Craft a workload with two jobs and scheduler parameters so that one job takes advantage of the older Rules 4a and 4b (turned on with the -s flag) to game the scheduler and obtain 99% of the CPU over a particular time interval. 5. Given a system with a quantum length of 10 ms in its highest queue, how often would you have to boost jobs back to the highest priority level (with the -B flag) in order to guarantee that a single long- running (and potentially-starving) job gets at least 5% of the CPU? 6. One question that arises in scheduling is which end of a queue to add a job that just finished I/O; the -I flag changes this behavior for this scheduling simulator. Play around with some workloads and see if you can see the effect of this flag. 12 SCHEDULING: THE MULTI-LEVEL FEEDBACK QUEUE Homework (Simulation) This program, mlfq.py, allows you to see how the MLFQ scheduler presented in this chapter behaves. See the README for details. Questions 1. Run a few randomly-generated problems with just two jobs and two queues; compute the MLFQ execution trace for each. Make your life easier by limiting the length of each job and turning off I/Os. 2. How would you run the scheduler to reproduce each of the exam- ples in the chapter? 3. How would you configure the scheduler parameters to behave just like a round-robin scheduler? 4. Craft a workload with two jobs and scheduler parameters so that one job takes advantage of the older Rules 4a and 4b (turned on with the -s flag) to game the scheduler and obtain 99% of the CPU over a particular time interval. 5. Given a system with a quantum length of 10 ms in its highest queue, how often would you have to boost jobs back to the highest priority level (with the -B flag) in order to guarantee that a single long- running (and potentially-starving) job gets at least 5% of the CPU? 6. One question that arises in scheduling is which end of a queue to add a job that just finished I/O; the -I flag changes this behavior for this scheduling simulator. Play around with some workloads and see if you can see the effect of this flag. 12 SCHEDULING: THE MULTI-LEVEL FEEDBACK QUEUE Homework (Simulation) This program, mlfg.py, allows you to see how the MLFQ scheduler presented in this chapter behaves. See the README for details. Questions 1. Run a few randomly-generated problems with just two jobs and two queues; compute the MLFQ execution trace for each. Make your life easier by limiting the length of each job and turning off /Os. 2. How would you run the scheduler to reproduce each of the exam- ples in the chapter? 3. How would you configure the scheduler parameters to behave just like a round-robin scheduler? 4. Craft a workload with two jobs and scheduler parameters so that one job takes advantage of the older Rules 4a and 4b (turned on with the -s flag) to game the scheduler and obtain 99% of the CPU over a particular time interval. 5. Given a system with a quantum length of 10 ms in its highest queue, how often would you have to boost jobs back to the highest priority level (with the -B flag) in order to guarantee that a single long- running (and potentially-starving) job gets at least 5% of the CPU? 6. One question that arises in scheduling is which end of a queue to add a job that just finished I/O; the -I flag changes this behavior for this scheduling simulator. Play around with some workloads and see if you can see the effect of this flag. 12 SCHEDULING: THE MULTI-LEVEL FEEDBACK QUEUE Homework (Simulation) This program, mlfq.py, allows you to see how the MLFQ scheduler presented in this chapter behaves. See the README for details. Questions 1. Run a few randomly-generated problems with just two jobs and two queues; compute the MLFQ execution trace for each. Make your life easier by limiting the length of each job and turning off I/Os. 2. How would you run the scheduler to reproduce each of the exam- ples in the chapter? 3. How would you configure the scheduler parameters to behave just like a round-robin scheduler? 4. Craft a workload with two jobs and scheduler parameters so that one job takes advantage of the older Rules 4a and 4b (turned on with the -s flag) to game the scheduler and obtain 99% of the CPU over a particular time interval. 5. Given a system with a quantum length of 10 ms in its highest queue, how often would you have to boost jobs back to the highest priority level (with the -B flag) in order to guarantee that a single long- running (and potentially-starving) job gets at least 5% of the CPU? 6. One question that arises in scheduling is which end of a queue to add a job that just finished I/O; the -I flag changes this behavior for this scheduling simulator. Play around with some workloads and see if you can see the effect of this flag