def __init__(self, caches, weights=None, **kwargs):
"""Constructor
Parameters
----------
caches : array-like
An array of caching nodes instances on the array
weights : array-like
Random weights according to which a cache of the array should be
selected to process a given request
"""
self._caches = caches
self._len = sum(len(c) for c in caches)
self._n_caches = len(caches)
self._selected_cache = None
if weights is not None:
if np.abs(np.sum(weights) - 1) > 0.0001:
raise ValueError("weights must sum up to 1")
if len(weights) != self._n_caches:
raise ValueError(
"weights must have as many elements as nr of caches")
randvar = DiscreteDist(weights)
self.select_cache = lambda: self._caches[randvar.rv() - 1]
else:
self.select_cache = lambda: random.choice(self._caches)
def numeric_cache_hit_ratio_2_layers(pdf, l1_cache, l2_cache,
warmup=None, measure=None, seed=None):
"""Numerically compute the cache hit ratio of a two-layer cache under IRM
stationary demand with a given pdf.
Differently from the numeric_cache_hit_ratio function, this function
allows users to compute the hits at layer 1, layer 2 and overall.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
Returns
-------
cache_hit_ratio : dict
Dictionary with keys "l1_hits", "l2_hits" and "total_hits"
"""
if warmup is None:
warmup = 10 * len(pdf)
if measure is None:
measure = 30 * len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not l1_cache.get(content):
if not l2_cache.get(content):
l2_cache.put(content)
l1_cache.put(content)
l1_hits = 0
l1_misses = 0
l2_hits = 0
for _ in range(measure):
content = z.rv()
if l1_cache.get(content):
l1_hits += 1
else:
l1_misses += 1
if l2_cache.get(content):
l2_hits += 1
else:
l2_cache.put(content)
l1_cache.put(content)
return {
'l1_hits': l1_hits / measure,
'l2_hits': l2_hits / measure,
'total_hits': (l1_hits + l2_hits) / measure
}
def numeric_cache_hit_ratio_2_layers(pdf, l1_cache, l2_cache,
warmup=None, measure=None, seed=None):
"""Numerically compute the cache hit ratio of a two-layer cache under IRM
stationary demand with a given pdf.
Differently from the numeric_cache_hit_ratio function, this function
allows users to compute the hits at layer 1, layer 2 and overall.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
Returns
-------
cache_hit_ratio : dict
Dictionary with keys "l1_hits", "l2_hits" and "total_hits"
"""
if warmup is None: warmup = 10*len(pdf)
if measure is None: measure = 30*len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not l1_cache.get(content):
if not l2_cache.get(content):
l2_cache.put(content)
l1_cache.put(content)
l1_hits = 0
l1_misses = 0
l2_hits = 0
for _ in range(measure):
content = z.rv()
if l1_cache.get(content):
l1_hits += 1
else:
l1_misses += 1
if l2_cache.get(content):
l2_hits += 1
else:
l2_cache.put(content)
l1_cache.put(content)
return {
'l1_hits': l1_hits/measure,
'l2_hits': l2_hits/measure,
'total_hits': (l1_hits+l2_hits)/measure
}
def numeric_per_content_cache_hit_ratio(pdf,
cache,
warmup=None,
measure=None,
seed=None,
target=None):
"""Numerically compute the per-content cache hit ratio of a cache under IRM
stationary demand with a given pdf.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
target : int, optional
The item index [1, N] for which cache hit ratio is requested. If not
specified, the function calculates the cache hit ratio of all the items
in the population.
Returns
-------
cache_hit_ratio : array of float or float
If target is None, returns an array with the cache hit ratios of all
items in the population. If a target is specified, then it returns
the cache hit ratio of only the specified item.
"""
if warmup is None:
warmup = 10 * len(pdf)
if measure is None:
measure = 30 * len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not cache.get(content):
cache.put(content)
cache_hits = np.zeros(len(pdf))
requests = np.zeros(len(pdf))
for _ in range(measure):
content = z.rv()
requests[content - 1] += 1
if cache.get(content):
cache_hits[content - 1] += 1
else:
cache.put(content)
hit_ratio = np.where(requests > 0, cache_hits / requests, requests)
return hit_ratio if target is None else hit_ratio[target - 1]
def numeric_per_content_cache_hit_ratio(pdf, cache, warmup=None, measure=None,
seed=None, target=None):
"""Numerically compute the per-content cache hit ratio of a cache under IRM
stationary demand with a given pdf.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
target : int, optional
The item index [1, N] for which cache hit ratio is requested. If not
specified, the function calculates the cache hit ratio of all the items
in the population.
Returns
-------
cache_hit_ratio : array of float or float
If target is None, returns an array with the cache hit ratios of all
items in the population. If a target is specified, then it returns
the cache hit ratio of only the specified item.
"""
if warmup is None: warmup = 10*len(pdf)
if measure is None: measure = 30*len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not cache.get(content):
cache.put(content)
cache_hits = np.zeros(len(pdf))
requests = np.zeros(len(pdf))
for _ in range(measure):
content = z.rv()
requests[content-1] += 1
if cache.get(content):
cache_hits[content-1] += 1
else:
cache.put(content)
hit_ratio = np.where(requests > 0, cache_hits/requests, requests)
return hit_ratio if target is None else hit_ratio[target-1]
def numeric_cache_hit_ratio(pdf, cache, warmup=None, measure=None, seed=None):
"""Numerically compute the cache hit ratio of a cache under IRM
stationary demand with a given pdf.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
Returns
-------
cache_hit_ratio : float
The cache hit ratio
"""
if warmup is None:
warmup = 10 * len(pdf)
if measure is None:
measure = 30 * len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not cache.get(content):
cache.put(content)
cache_hits = 0
for _ in range(measure):
content = z.rv()
if cache.get(content):
cache_hits += 1
else:
cache.put(content)
return cache_hits / measure
def numeric_cache_hit_ratio(pdf, cache, warmup=None, measure=None, seed=None):
"""Numerically compute the cache hit ratio of a cache under IRM
stationary demand with a given pdf.
Parameters
----------
pdf : array-like
The probability density function of an item being requested
cache : Cache
The cache object (i.e. the instance of a class subclassing
icarus.Cache)
warmup : int, optional
The number of warmup requests to generate. If not specified, it is set
to 10 times the content population
measure : int, optional
The number of measured requests to generate. If not specified, it is
set to 30 times the content population
seed : int, optional
The seed used to generate random numbers
Returns
-------
cache_hit_ratio : float
The cache hit ratio
"""
if warmup is None: warmup = 10*len(pdf)
if measure is None: measure = 30*len(pdf)
z = DiscreteDist(pdf, seed)
for _ in range(warmup):
content = z.rv()
if not cache.get(content):
cache.put(content)
cache_hits = 0
for _ in range(measure):
content = z.rv()
if cache.get(content):
cache_hits += 1
else:
cache.put(content)
return cache_hits/measure
def __init__(self, caches, weights=None, **kwargs):
"""Constructor
Parameters
----------
caches : array-like
An array of caching nodes instances on the array
weights : array-like
Random weights according to which a cache of the array should be
selected to process a given request
"""
self._caches = caches
self._len = sum(len(c) for c in caches)
self._n_caches = len(caches)
self._selected_cache = None
if weights is not None:
if np.abs(np.sum(weights) - 1) > 0.0001:
raise ValueError("weights must sum up to 1")
if len(weights) != self._n_caches:
raise ValueError("weights must have as many elements as nr of caches")
randvar = DiscreteDist(weights)
self.select_cache = lambda: self._caches[randvar.rv() - 1]
else:
self.select_cache = lambda: random.choice(self._caches)
