import numpy as np
# Define the parameters of the caching problem
N = 100 # number of end user devices
M = 10 # number of edge devices
C = 10000 # cache capacity of each edge device in bytes
F = np.random.randint(1, 10, size=(N, M)) # access frequency matrix
S = np.random.randint(100, 1000, size=N) # content size array
# Define the heuristic algorithm to solve the caching optimization problem
def cache_heuristic(N, M, C, F, S):
# Calculate the popularity score for each content
P = np.sum(F, axis=0)
# Sort the contents by their popularity scores
sorted_contents = np.argsort(P)[::-1]
# Initialize the cache of each edge device to be empty
cache = np.zeros((M, C))
# For each content in the sorted list, starting from the most popular content
for j in sorted_contents:
# Check if content j can fit in any of the edge devices' caches
added_to_cache = False
for k in range(M):
if np.sum(cache[k]) + S[j] <= C:
cache[k][np.argmax(cache[k] == 0)] = S[j] * F[:,j].sum()
added_to_cache = True
break
# If content j cannot fit in any of the edge devices' caches, use LRU replacement policy
if not added_to_cache:
least_popular_device = np.argmin(P)
least_popular_content_index = np.argmin(cache[least_popular_device])
cache[least_popular_device][least_popular_content_index] = S[j] * F[:,j].sum()
# Return the cached contents on each edge device
cached_contents = []
for k in range(M):
cached_contents.append(np.where(cache[k] > 0)[0])
return cached_contents
# Call the heuristic algorithm and print the results
cached_contents = cache_heuristic(N, M, C, F, S)
for k in range(M):
print(f"Edge device {k}: Cached contents {cached_contents[k]}")
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