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def fcfs_scheduling(processes, n):
processes.sort(key=lambda x: x[1])
completion_time = 0
waiting_time = []
turnaround_time = []
for process in processes:
pid, arrival_time, burst_time = process
if completion_time < arrival_time:
completion_time = arrival_time
completion_time += burst_time
turnaround_time.append(completion_time - arrival_time)
waiting_time.append(completion_time - arrival_time - burst_time)
print("\nFCFS Scheduling:")
for i, process in enumerate(processes):
print(f"Process {process[0]}: Waiting Time = {waiting_time[i]}, Turnaround Time = {turnaround_time[i]}")
# Input: Process ID, Arrival Time, Burst Time
processes = [[1, 0, 5], [2, 1, 3], [3, 2, 8], [4, 3, 6]]
fcfs_scheduling(processes, len(processes))

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def priority_scheduling(processes, n):
processes.sort(key=lambda x: (x[2], x[1]))
completion_time = 0
waiting_time = []
turnaround_time = []
for process in processes:
pid, arrival_time, priority, burst_time = process
if completion_time < arrival_time:
completion_time = arrival_time
completion_time += burst_time
turnaround_time.append(completion_time - arrival_time)
waiting_time.append(completion_time - arrival_time - burst_time)
print("\nPriority (Non-Preemptive) Scheduling:")
for i, process in enumerate(processes):
print(f"Process {process[0]}: Waiting Time = {waiting_time[i]}, Turnaround Time = {turnaround_time[i]}")
# Input: Process ID, Arrival Time, Priority, Burst Time
processes = [[1, 0, 1, 5], [2, 1, 3, 3], [3, 2, 2, 8], [4, 3, 4, 6]]
priority_scheduling(processes, len(processes))

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def round_robin(processes, n, quantum):
remaining_time = [bt for _, _, bt in processes]
t = 0
waiting_time = [0] * n
turnaround_time = [0] * n
complete = [False] * n
while True:
done = True
for i in range(n):
if remaining_time[i] > 0:
done = False
if remaining_time[i] > quantum:
t += quantum
remaining_time[i] -= quantum
else:
t += remaining_time[i]
waiting_time[i] = t - processes[i][2] - processes[i][1]
remaining_time[i] = 0
if done:
break
for i in range(n):
turnaround_time[i] = processes[i][2] + waiting_time[i]
print("\nRound Robin (Preemptive) Scheduling:")
for i, process in enumerate(processes):
print(f"Process {process[0]}: Waiting Time = {waiting_time[i]}, Turnaround Time = {turnaround_time[i]}")
# Input: Process ID, Arrival Time, Burst Time
processes = [[1, 0, 10], [2, 1, 4], [3, 2, 5], [4, 3, 3]]
quantum = 2
round_robin(processes, len(processes), quantum)

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import sys
def sjf_preemptive(processes, n):
remaining_time = [bt for _, _, bt in processes]
complete = 0
t = 0
minm = sys.maxsize
shortest = 0
finish_time = 0
check = False
waiting_time = [0] * n
turnaround_time = [0] * n
while complete != n:
for j in range(n):
if processes[j][1] <= t and remaining_time[j] < minm and remaining_time[j] > 0:
minm = remaining_time[j]
shortest = j
check = True
if not check:
t += 1
continue
remaining_time[shortest] -= 1
minm = remaining_time[shortest] if remaining_time[shortest] > 0 else sys.maxsize
if remaining_time[shortest] == 0:
complete += 1
check = False
finish_time = t + 1
waiting_time[shortest] = finish_time - processes[shortest][2] - processes[shortest][1]
if waiting_time[shortest] < 0:
waiting_time[shortest] = 0
t += 1
for i in range(n):
turnaround_time[i] = processes[i][2] + waiting_time[i]
print("\nSJF (Preemptive) Scheduling:")
for i, process in enumerate(processes):
print(f"Process {process[0]}: Waiting Time = {waiting_time[i]}, Turnaround Time = {turnaround_time[i]}")
# Input: Process ID, Arrival Time, Burst Time
processes = [[1, 0, 8], [2, 1, 4], [3, 2, 9], [4, 3, 5]]
sjf_preemptive(processes, len(processes))

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def best_fit(memory_blocks, process_sizes):
allocation = [-1] * len(process_sizes)
for i in range(len(process_sizes)):
best_idx = -1
best_size = float('inf')
for j in range(len(memory_blocks)):
if memory_blocks[j] >= process_sizes[i] and memory_blocks[j] - process_sizes[i] < best_size:
best_size = memory_blocks[j] - process_sizes[i]
best_idx = j
if best_idx != -1:
allocation[i] = best_idx
memory_blocks[best_idx] -= process_sizes[i]
print("\nBest Fit Allocation:")
for i in range(len(process_sizes)):
if allocation[i] != -1:
print(f"Process {i+1} allocated to Block {allocation[i]+1}")
else:
print(f"Process {i+1} not allocated")
# Example Memory Blocks and Process Sizes
memory_blocks = [100, 500, 200, 300, 600]
process_sizes = [212, 417, 112, 426]
best_fit(memory_blocks, process_sizes)

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def first_fit(memory_blocks, process_sizes):
allocation = [-1] * len(process_sizes)
for i in range(len(process_sizes)):
for j in range(len(memory_blocks)):
if memory_blocks[j] >= process_sizes[i]:
allocation[i] = j
memory_blocks[j] -= process_sizes[i]
break
print("\nFirst Fit Allocation:")
for i in range(len(process_sizes)):
if allocation[i] != -1:
print(f"Process {i+1} allocated to Block {allocation[i]+1}")
else:
print(f"Process {i+1} not allocated")
# Example Memory Blocks and Process Sizes
memory_blocks = [100, 500, 200, 300, 600]
process_sizes = [212, 417, 112, 426]
first_fit(memory_blocks, process_sizes)

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def next_fit(memory_blocks, process_sizes):
allocation = [-1] * len(process_sizes)
next_index = 0
for i in range(len(process_sizes)):
while next_index < len(memory_blocks):
if memory_blocks[next_index] >= process_sizes[i]:
allocation[i] = next_index
memory_blocks[next_index] -= process_sizes[i]
next_index = (next_index + 1) % len(memory_blocks) # Move to next block
break
next_index += 1
print("\nNext Fit Allocation:")
for i in range(len(process_sizes)):
if allocation[i] != -1:
print(f"Process {i+1} allocated to Block {allocation[i]+1}")
else:
print(f"Process {i+1} not allocated")
# Example Memory Blocks and Process Sizes
memory_blocks = [100, 500, 200, 300, 600]
process_sizes = [212, 417, 112, 426]
next_fit(memory_blocks, process_sizes)

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def worst_fit(memory_blocks, process_sizes):
allocation = [-1] * len(process_sizes)
for i in range(len(process_sizes)):
worst_idx = -1
worst_size = -1
for j in range(len(memory_blocks)):
if memory_blocks[j] >= process_sizes[i] and memory_blocks[j] > worst_size:
worst_size = memory_blocks[j]
worst_idx = j
if worst_idx != -1:
allocation[i] = worst_idx
memory_blocks[worst_idx] -= process_sizes[i]
print("\nWorst Fit Allocation:")
for i in range(len(process_sizes)):
if allocation[i] != -1:
print(f"Process {i+1} allocated to Block {allocation[i]+1}")
else:
print(f"Process {i+1} not allocated")
# Example Memory Blocks and Process Sizes
memory_blocks = [100, 500, 200, 300, 600]
process_sizes = [212, 417, 112, 426]
worst_fit(memory_blocks, process_sizes)

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class LRU:
def __init__(self, capacity):
self.capacity = capacity
self.cache = {}
self.order = []
def refer(self, page):
if page not in self.cache:
if len(self.cache) >= self.capacity:
lru_page = self.order.pop(0) # Remove least recently used page
del self.cache[lru_page]
else:
self.order.remove(page) # Remove page to update its order
self.cache[page] = True
self.order.append(page)
def display(self):
print("Current Pages in Memory (LRU):", list(self.cache.keys()))
# Simulate LRU
def lru_simulation(pages, capacity):
lru = LRU(capacity)
for page in pages:
lru.refer(page)
lru.display()
# Example Pages and Capacity
pages = [7, 0, 1, 2, 0, 3, 0, 4]
capacity = 3
print("LRU Page Replacement Simulation:")
lru_simulation(pages, capacity)

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class Optimal:
def __init__(self, capacity):
self.capacity = capacity
self.cache = []
def refer(self, page, future):
if page not in self.cache:
if len(self.cache) < self.capacity:
self.cache.append(page)
else:
farthest_page = self.find_farthest_page(future)
index = self.cache.index(farthest_page)
self.cache[index] = page
def find_farthest_page(self, future):
farthest = -1
farthest_page = None
for page in self.cache:
if page not in future:
return page
try:
index = future.index(page)
if index > farthest:
farthest = index
farthest_page = page
except ValueError:
continue
return farthest_page
def display(self):
print("Current Pages in Memory (Optimal):", self.cache)
# Simulate Optimal Page Replacement
def optimal_simulation(pages, capacity):
optimal = Optimal(capacity)
for i in range(len(pages)):
optimal.refer(pages[i], pages[i + 1:])
optimal.display()
# Example Pages and Capacity
pages = [7, 0, 1, 2, 0, 3, 0, 4]
capacity = 3
print("\nOptimal Page Replacement Simulation:")
optimal_simulation(pages, capacity)