cachingalgo 0.0.2

Package consists of implementation of caching algorithms and request generation modules

cachingalgo

This project contains implementation different caching algorithms and request generation module to analyze the caching algorithms

Installation

Run the following to install the package:

pip install cachingalgo

or Run the following to install in the Google Colab:

# Clone the repo.
!git clone https://github.com/SuryaKrishna02/caching-algorithms.git

# Change the working directory to the repo root.
%cd cachingalgo

# Add the repo root to the Python path.
import sys, os
sys.path.append(os.getcwd())

Github Repository Notebooks

• Full Observation Single Cache: LFU, WLFU, LFULite, CountSketch
• Full Observation Multiple Cache: LRU, fLRU, LRUm
• Partial Observation Single Cache: CBMPS, CBSI, CBSILite
• Tandem Model Static Data: LFU-LFU, LFULite-LFULite
• Tandem Model Dynamic Data: LU2, LU2-LFU, LU2-LFULite
• Dynamic Data: LU, LU-LFU, LU-LFULite[C or 2C or 3C], LU-LFULite(V)[C or 2C or 3C]
• Cost Minimization: CMSR, CMDR, CMDRM, CMDRP

Request Generation

from cachingalgo.request_generation.continuous import szipf, dzipf, youtube, netflix

# To generate requests which follows Static Zipf distribution.
# 50,000 requests with library size = 1,61,085 and Zipf shape parameter = 1

szdata = szipf(count=50_000, a=1, L=1_61_085)
# Probabilty distribution
szdataprob = szdata['prob']
# Array of requests
szdatareq = szdata['req']


# To generate requests which follows Static Zipf distribution but with changing popularity.
# 50,000 requests with library size = 1,61,085 and popularity changes for every 5,000 requests
# by shifting probabilities of the top 500 items by 50.

dzdata = dzipf(count=50_000, a=1, L=1_61_085, req_step=5_000, window=500, top=50, cache_size=10)
# Array of requests
dzdatareq = dzdata['req']
# Array of size (count//req_step)*cache_size i.e optimal cache for each req_step requests
optimalcache = dzdata['optim_cache']


# To generate requests from the YouTube data.
# 50,000 requests with the library size = 1,61,085

ytdata = youtube(count=50_000)
# Probability distribution
ytdataprob = ytdata['prob']
# Array of requests
ytdatareq = ytdata['req']


# To generate requests from the Netflix data.
# 50,000 requests with the library size = 17,770

netdata = netflix(count=50_000)
# Probability distribution
netdataprob = netdata['prob']
# Array of requests
netdatareq = netdata['req']

We can use any of the four types of requests for analyzing the caching algorithms. In this documentation, we will use requests generated from the YouTube data.

Usage of LFU algorithm

from cachingalgo.full_observation.single_cache import LFU

# Initialising the algorithm
alg = LFU(L=100, cache_size=5)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)
    # Returns the cache at current instant
    currcache = alg.currcache()

Usage of WLFU algorithm

from cachingalgo.full_observation.single_cache import WLFU

# Initialising the algorithm
alg = WLFU(L=100, cache_size=5)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)
    # Returns the cache at current instant
    currcache = alg.currcache()

Usage of LFU-Lite algorithm

from cachingalgo.full_observation.single_cache import LFULite

# Initialising the algorithm
alg = LFULite(L=100, cache_size=5)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request, i)
    # Returns the cache at current instant
    currcache = alg.currcache(i)

Usage of LRU algorithm

from cachingalgo.full_observation.single_cache import LRU

# Initialising the algorithm
alg = LRU(L=100, cache_size=5)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)

    # To check whether a request is present in the cache
    present = request in alg

Usage of Count-Sketch algorithm

from cachingalgo.full_observation.single_cache import CountSketch

# Initialising the algorithm
alg = CountSketch(l=6, b=10, L=100)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)
    # Returns the cache at current instant
    currcache = alg.currcache(request)

Usage of f-LRU algorithm

from cachingalgo.full_observation.multiple_cache import fLRU

# Initialising the algorithm
alg = fLRU(L=100, size=5, f=2)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)

    # To check whether a request is present in the cache
    present = request in alg

Usage of LRU(m) algorithm

from cachingalgo.full_observation.multiple_cache import LRUm

# Initialising the algorithm
alg = LRUm(L=100, size=5, f=2, v=1)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)

    # To check whether a request is present in the cache
    present = request in alg

Usage of CB-MPS algorithm

from cachingalgo.partial_observation.single_cache import CBMPS

# Initialising the algorithm
alg = CBMPS(L=100, cache_size=5)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Returns the cache at current instant
    currcache = alg.currcache(request)
    # Updates the counter
    alg.update(request)

Usage of CB-SI algorithm

from cachingalgo.partial_observation.single_cache import CBSI, calculate_delta

# Calculate the parameter which are to be provided for the CB-SI algorithms
delta, mu_c = calculate_delta(ytdataprob, cache_size=10)
# Initialising the algorithm
alg = CBSI(L=100, cache_size=5, delta=delta, mu_c=mu_c)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Returns the cache at current instant
    currcache = alg.currcache(request)
    # Updates the counter
    alg.update(request)

Usage of CB-SILite algorithm

from cachingalgo.partial_observation.single_cache import CBSILite, calculate_delta

# Calculate the parameter which are to be provided for the CB-SI algorithms
delta, mu_c = calculate_delta(ytdataprob, cache_size=10)
# Initialising the algorithm
alg = CBSILite(L=100, cache_size=5, delta=delta, mu_c=mu_c)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # update the counterbank
    alg.counterbank_update(request)
    # Returns the cache at current instant
    currcache = alg.currcache(request)
    # Updates the counter
    alg.update(request)

Usage of LU algorithm

from cachingalgo.full_observation.single_cache import LU

# Defining the parameters
L = 100
cache_size = 5

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU(L=L, F=F, cache_size=cache_size, arr=ytdataprob)
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i)

Usage of LU-LFU algorithm

from cachingalgo.full_observation.single_cache import LU

# Defining the parameters
L = 100
cache_size = 5

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU(L=L, F=F, cache_size=cache_size, method='lfu')
# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i)

Usage of LU-LFULite[C or 2C or 3C] algorithm

from cachingalgo.full_observation.single_cache import LU
import math
# Defining the parameters
L = 100
cache_size = 5
window = int((cache_size^2)*math.log(L))

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU(L=L, F=F, cache_size=cache_size, method='lfulite', window=window, freqtop=cache_size)

# In the above initialisation changing freqtop, one can get
# freqtop = 2*cache_size - LU-LFULite[2C] algorithm
# freqtop = 3*cache_size - LU-LFULite[3C] algorithm

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request, ithreq=i)
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i, ithreq=i)

Usage of LU-LFULite(V)[C or 2C or 3C] algorithm

from cachingalgo.full_observation.single_cache import LU
import math
# Defining the parameters
L = 100
cache_size = 5
window = int((cache_size^2)*math.log(L))

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU(L=L, F=F, cache_size=cache_size, method='lfulite', window=window, freqtop=cache_size, useF=True)

# In the above initialisation changing freqtop, one can get
# freqtop = 2*cache_size - LU-LFULite(V)[2C] algorithm
# freqtop = 3*cache_size - LU-LFULite(V)[3C] algorithm

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request, ithreq=i)
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i, ithreq=i)

Usage of LU2 algorithm

from cachingalgo.tandem_model.dynamic import LU2

# Defining the parameters
L = 100

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU2(L=L, F=F, cache_sizes=[5, 10], arr=ytdataprob)

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i)

Usage of LU2-LFU algorithm

from cachingalgo.tandem_model.dynamic import LU2

# Defining the parameters
L = 100

# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU2(L=L, F=F, cache_sizes=[5, 10], method='lfu')

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request)
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i)

Usage of LU2-LFULite(V)[3C] algorithm

from cachingalgo.tandem_model.dynamic import LU2
import math

# Defining the parameters
L = 100
# 5 is the size of cache 1
window = int((5^2)*math.log(L))
# Freshness Constraint array
F = np.ones((L,), dtype=int)

# Initialising the algorithm
alg = LU2(L=L, F=F, cache_sizes=[5, 10], method='lfulite', window=window, fretop=3*5, useF=True)

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
    request = ytdatareq[i]
    # Updates the counter
    alg.update(request, ithreq=i)
    # Returns the cache and hit or miss information at current instant
    currcache = alg.currcache(req=request, time=i, ithreq=i)

Usage of CMSR algorithm

from cachingalgo.full_observation.cost_min import CMSR

# Initialising the algorithm
alg = CMSR(cache_size=5, L=100, beta=10, z=1.5, lambda_param=5, Cost=[1, 0.1, 0.05, 0.025], prob=ytdataprob, seed=7)

# To retrieve the cache
algcache = alg.currcache()

# To calculate the average system cost
avg_sys_cost = alg.avg_sys_cost(alg.mu_hat)

Usage of CMDR algorithm

from cachingalgo.full_observation.cost_min import CMDR

# Defining the parameter
cache_size = 5

# Initialising the algorithm
alg = CMDR(cache_size=cache_size, L=100, beta=10, z=1.5, lambda_param=5, Cost=[1, 0.1, 0.05, 0.025], prob=ytdataprob, C_hat=np.arange(cache_size, dtype='int'), seed=7)

# To retrieve the cache
algcache = alg.currcache()

# To calculate the average system cost
avg_sys_cost = alg.avg_sys_cost(alg.mu_hat)

Usage of CMDRM algorithm

from cachingalgo.full_observation.cost_min import CMDR

# Defining the parameter
cache_size = 5

# Initialising the algorithm
alg = CMDR(cache_size=cache_size, L=100, beta=10, z=1.5, lambda_param=5, Cost=[1, 0.1, 0.05, 0.025], cache_update='marg', seed=7)

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
  request = ytdatareq[i]

  # To retrieve the cache
  algcache = alg.currcache(request)

  # To calculate the average system cost
  avg_sys_cost = alg.avg_sys_cost(alg.mu_hat)

Usage of CMDRP algorithm

from cachingalgo.full_observation.cost_min import CMDR

# Defining the parameter
cache_size = 5

# Initialising the algorithm
alg = CMDR(cache_size=cache_size, L=100, beta=10, z=1.5, lambda_param=5, Cost=[1, 0.1, 0.05, 0.025], cache_update='pop', seed=7)

# Total no. of requests
totalreq = ytdatareq.shape[0]

for i in range(totalreq):
  request = ytdatareq[i]

  # To retrieve the cache
  algcache = alg.currcache(request)

  # To calculate the average system cost
  avg_sys_cost = alg.avg_sys_cost(alg.mu_hat)