def simulate(jobs):
print("Shortest Job Next (SJN) Information:")
# initialize variables
results = []
current_cycle = 0
job_id = 1
# don't overwrite the main jobs array
jobs = util.deepcopy(jobs)
# sort jobs by arrival
jobs.sort(key=lambda x: x[0])
# add Job IDs to jobs
i = 0
for job in jobs:
job.append(i)
i += 1
# start our queue
jobs = deque(jobs)
job_queue = []
heapq.heapify(job_queue)
while len(jobs) > 0 and jobs[0][0] == current_cycle:
job_queue.append(jobs.popleft())
while len(job_queue) > 0:
# grab the next job to process (sort by priority first)
job_queue.sort(key=lambda x: x[1])
job = heapq.heappop(job_queue)
job_arrived = job[0]
cycles_to_run = job[1]
job_id = job[2]
# simulate each cycle
while cycles_to_run > 0:
current_cycle += 1
cycles_to_run -= 1
# if a job is available, move it to the queue
while len(jobs) > 0 and jobs[0][0] == current_cycle:
job_queue.append(jobs.popleft())
results.append([job_id, current_cycle - job_arrived])
results.sort(key=lambda x: x[0])
for result in results:
print("Job {1} Turn-around Time: {0} ".format(result[1], chr(result[0] + 65)))
time = mean([result[1] for result in results])
print("Average Turn-around Time: {0}\n".format(time))
return time
def simulate(jobs, time_quantum):
print("Round Robin Information (Time Quantum = {0}):".format(time_quantum))
# initialize variables
results = []
current_cycle = 0
job_id = 1
# don't overwrite the main jobs array
jobs = util.deepcopy(jobs)
# sort jobs by arrival
jobs.sort(key=lambda x: x[0])
# add Job IDs to jobs
i = 0
for job in jobs:
job.append(i)
i += 1
# start our queue
jobs = deque(jobs)
job_queue = deque()
while len(jobs) > 0 and jobs[0][0] == current_cycle:
job_queue.append(jobs.popleft())
#print("Cycle\tJob\t\tCycles Left\t\tQuantum Left")
while len(job_queue) > 0:
# grab the next job to process
job = job_queue.popleft()
job_arrived = job[0]
cycles_to_run = job[1]
job_id = job[2]
# new job starting, reset time quantum
time_quantum_left = time_quantum
# simulate each cycle
preempt = False
while cycles_to_run > 0 and not preempt:
current_cycle += 1
cycles_to_run -= 1
time_quantum_left -= 1
#print("{0}\t\t{1}\t\t{3}\t\t\t\t{2}".format(current_cycle, chr(job_id+65), time_quantum_left, cycles_to_run))
#print(job_queue)
# if a job is available, move it to the queue
while len(jobs) > 0 and jobs[0][0] == current_cycle:
new_job = jobs.popleft()
job_queue.append(new_job)
# check if we need to preempt
if time_quantum_left == 0 and cycles_to_run > 0:
job_queue.append([job_arrived, cycles_to_run, job_id])
preempt = True
if cycles_to_run == 0 and not preempt:
results.append([job_id, current_cycle - job_arrived])
results.sort(key=lambda x: x[0])
for result in results:
print("Job {1} Turn-around Time: {0} ".format(result[1], chr(result[0] + 65)))
time = mean([result[1] for result in results])
print("Average Turn-around Time: {0}\n".format(time))
return time
