class LaCReME_context1(page_replacement_algorithm):
def __init__(self, N):
self.N = N
self.CacheRecency = Disk(N)
self.CacheFrequecy = priorityqueue(N)
self.Hist1 = Disk(N)
self.Hist2 = priorityqueue(N)
## Config variables
self.decayRate = 1
self.epsilon = 0.90
self.lamb = 0.05
self.learning_phase = N/2
self.error_discount_rate = (0.005)**(1.0/N)
# self.error_discount_rate = 1
##
self.learning = True
self.policy = 0
self.evictionTime = {}
self.policyUsed = {}
self.weightsUsed = {}
self.freq = {}
## TODO add decay_time and decay_factor
self.decay_time = N
self.decay_factor = 1
## Accounting variables
self.time = 0
self.W = np.zeros((10,2))
self.W[:,:] = 0.5
self.X = np.array([],dtype=np.int32)
self.Y1 = np.array([])
self.Y2 = np.array([])
###
self.q = Queue.Queue()
self.sum = 0
self.NewPages = []
def get_N(self) :
return self.N
def visualize(self, plt):
# print(np.min(self.X), np.max(self.X))
ax = plt.subplot(2,1,1)
ax.set_xlim(np.min(self.X), np.max(self.X))
l1, = plt.plot(self.X,self.Y1, 'b-', label='W_lru')
l2, = plt.plot(self.X,self.Y2, 'r-', label='W_lfu')
l3, = plt.plot(self.X, self.NewPages, 'g-', label='New Pages')
return [l1,l2,l3]
def __keyWithMinVal(self,d):
v=list(d.values())
k=list(d.keys())
return k[v.index(min(v))]
def getMinValueFromCache(self, values):
minpage,first = -1, True
for q in self.Cache :
if first or values[q] < values[minpage] :
minpage,first=q,False
return minpage
##########################################
## Add a page to cache using policy 'poly'
##########################################
def addToCache(self, page,pagefreq=0):
self.CacheRecency.add(page)
self.CacheFrequecy.add(page)
self.CacheRecency.increaseCount(page, amount=pagefreq)
self.CacheFrequecy.increase(page, amount=pagefreq)
######################
## Get LFU or LFU page
######################
def selectEvictPage(self, policy):
r = self.CacheRecency.getIthPage(0)
f = self.CacheFrequecy.peaktop()
pageToEvit,policyUsed = None, None
if r == f :
pageToEvit,policyUsed = r,-1
elif policy == 0:
pageToEvit,policyUsed = r,0
elif policy == 1:
pageToEvit,policyUsed = f,1
return pageToEvit,policyUsed
def evictPage(self, pg):
self.CacheRecency.delete(pg)
self.CacheFrequecy.delete(pg)
##############################################################
## There was a page hit to 'page'. Update the data structures
##############################################################
def pageHitUpdate(self, page):
if page in self.CacheRecency and page in self.CacheFrequecy:
self.CacheRecency.moveBack(page)
self.CacheRecency.increaseCount(page)
self.CacheFrequecy.increase(page)
#########################
## Get the Q distribution
#########################
def getQ(self,wid):
return (1-self.lamb) * self.W[wid] + self.lamb*np.ones(2)/2
############################################
## Choose a page based on the q distribution
############################################
def chooseRandom(self, wid):
q = self.getQ(wid)
r = np.random.rand()
for i,p in enumerate(q):
if r < p:
return i
return len(q)-1
def updateWeight(self, cost):
self.W = self.W * (1-self.epsilon * cost)
self.W = self.W / np.sum(self.W)
########################################################################################################################################
####REQUEST#############################################################################################################################
########################################################################################################################################
def request(self,page) :
page_fault = False
self.time = self.time + 1
# if self.time % self.learning_phase == 0 :
# self.learning = not self.learning
partitions = 5
newpagesratio = 1.0*self.sum / self.N
weigthid = int(newpagesratio*partitions) if newpagesratio < 1.0 else partitions-1
#####################
## Visualization data
#####################
prob = self.getQ(weigthid)
self.X = np.append(self.X, self.time)
self.Y1 = np.append(self.Y1, prob[0])
self.Y2 = np.append(self.Y2, prob[1])
notInHistory = 0
##########################
## Process page request
##########################
if page in self.CacheFrequecy:
page_fault = False
self.pageHitUpdate(page)
else :
#####################################################
## Learning step: If there is a page fault in history
#####################################################
pageevict, histpage_freq = None,1
policyUsed = -1
if page in self.Hist1:
pageevict = page
histpage_freq = self.Hist1.getCount(page)
self.Hist1.delete(page)
policyUsed = 0
elif page in self.Hist2:
pageevict = page
histpage_freq = self.Hist2.getFreq(page) ## Get the page frequency in history
self.Hist2.delete(page)
policyUsed = 1
else:
notInHistory = 1
if pageevict is not None :
q = self.weightsUsed[pageevict]
err = self.error_discount_rate ** (self.time - self.evictionTime[pageevict])
reward = np.array([0,0], dtype=np.float32)
if policyUsed == 0 : # LRU
reward[1] = err
if policyUsed == 1:
reward[0] = err
reward_hat = reward / q
#################
## Update Weights
#################
if self.policyUsed[pageevict] != -1 :
self.W[weigthid] = self.W[weigthid] * np.exp(self.lamb * reward_hat / 2)
self.W[weigthid] = self.W[weigthid] / np.sum(self.W[weigthid])
####################
## Remove from Cache
####################
if self.CacheRecency.size() == self.N:
################
## Choose Policy
################
if not self.learning :
act = np.argmax(self.W[weigthid])
else :
act = self.chooseRandom(weigthid)
cacheevict,poly = self.selectEvictPage(act)
pagefreq = self.CacheFrequecy.getCount(cacheevict)
self.policyUsed[cacheevict] = poly
self.weightsUsed[cacheevict] = self.getQ(weigthid)
self.evictionTime[cacheevict] = self.time
if not self.learning :
self.policyUsed[cacheevict] = -1
###################
## Evict to history
###################
histevict = None
if (poly == 0) or (poly==-1 and np.random.rand() <0.5):
if self.Hist1.size() == self.N :
histevict = self.Hist1.getIthPage(0)
self.Hist1.delete(histevict)
self.Hist1.add(cacheevict)
self.Hist1.setCount(cacheevict, pagefreq)
else:
if self.Hist2.size() == self.N :
histevict = self.Hist2.popmin()
self.Hist2.add(cacheevict)
self.Hist2.increase(cacheevict, pagefreq-1)
if histevict is not None :
del self.evictionTime[histevict]
del self.policyUsed[histevict]
del self.weightsUsed[histevict]
self.evictPage(cacheevict)
self.addToCache(page, pagefreq=histpage_freq)
page_fault = True
self.q.put(notInHistory)
self.sum += notInHistory
if self.q.qsize() > self.N:
self.sum -= self.q.get()
self.NewPages.append(newpagesratio)
return page_fault
def get_list_labels(self) :
return ['L']
class LaCReME(page_replacement_algorithm):
def __init__(self, N):
self.N = N
self.CacheRecency = Disk(N)
self.CacheFrequecy = priorityqueue(N)
self.Hist1 = Disk(N)
self.Hist2 = priorityqueue(N)
## Config variables
self.decayRate = 1
self.epsilon = 0.05
self.lamb = 0.05
self.learning_phase = N / 2
# self.error_discount_rate = (0.005)**(1.0/N) ## TODO ADD BACK
self.error_discount_rate = 1
##
self.learning = True
self.policy = 0
self.evictionTime = {}
self.policyUsed = {}
self.weightsUsed = {}
self.freq = {}
## TODO add decay_time and decay_factor
self.decay_time = N
self.decay_factor = 1
## Accounting variables
self.time = 0
self.W = np.array([.5, .5], dtype=np.float32)
self.X = np.array([], dtype=np.int32)
self.Y1 = np.array([])
self.Y2 = np.array([])
def get_N(self):
return self.N
def visualize(self, plt):
# print(np.min(self.X), np.max(self.X))
ax = plt.subplot(2, 1, 1)
ax.set_xlim(np.min(self.X), np.max(self.X))
l1, = plt.plot(self.X, self.Y1, 'b-', label='W_lru')
l2, = plt.plot(self.X, self.Y2, 'r-', label='W_lfu')
return [l1, l2]
##########################################
## Add a page to cache using policy 'poly'
##########################################
def addToCache(self, page, pagefreq=0):
self.CacheRecency.add(page)
self.CacheFrequecy.add(page)
self.CacheRecency.increaseCount(page, amount=pagefreq)
self.CacheFrequecy.increase(page, amount=pagefreq)
######################
## Get LFU or LFU page
######################
def selectEvictPage(self, policy):
r = self.CacheRecency.getIthPage(0)
f = self.CacheFrequecy.peaktop()
pageToEvit, policyUsed = None, None
if r == f:
pageToEvit, policyUsed = r, -1
elif policy == 0:
pageToEvit, policyUsed = r, 0
elif policy == 1:
pageToEvit, policyUsed = f, 1
return pageToEvit, policyUsed
def evictPage(self, pg):
self.CacheRecency.delete(pg)
self.CacheFrequecy.delete(pg)
##############################################################
## There was a page hit to 'page'. Update the data structures
##############################################################
def pageHitUpdate(self, page):
if page in self.CacheRecency and page in self.CacheFrequecy:
self.CacheRecency.moveBack(page)
self.CacheRecency.increaseCount(page)
self.CacheFrequecy.increase(page)
#########################
## Get the Q distribution
#########################
def getQ(self):
return (1 - self.lamb) * self.W + self.lamb * np.ones(2) / 2
############################################
## Choose a page based on the q distribution
############################################
def chooseRandom(self):
q = self.getQ()
r = np.random.rand()
for i, p in enumerate(q):
if r < p:
return i
return len(q) - 1
def updateWeight(self, cost):
self.W = self.W * (1 - self.epsilon * cost)
self.W = self.W / np.sum(self.W)
########################################################################################################################################
####REQUEST#############################################################################################################################
########################################################################################################################################
def request(self, page):
page_fault = False
self.time = self.time + 1
# if self.time % self.learning_phase == 0 :
# self.learning = not self.learning
#####################
## Visualization data
#####################
prob = self.getQ()
self.X = np.append(self.X, self.time)
self.Y1 = np.append(self.Y1, prob[0])
self.Y2 = np.append(self.Y2, prob[1])
if self.time % self.N == 0:
self.CacheFrequecy.decay(self.decay_factor)
self.Hist2.decay(self.decay_factor)
##########################
## Process page request
##########################
if page in self.CacheFrequecy:
page_fault = False
self.pageHitUpdate(page)
else:
#####################################################
## Learning step: If there is a page fault in history
#####################################################
pageevict, histpage_freq = None, 1
policyUsed = -1
if page in self.Hist1:
pageevict = page
histpage_freq = self.Hist1.getCount(page)
self.Hist1.delete(page)
policyUsed = 0
self.W[0] = self.W[0] * (
1 -
self.epsilon) if self.policyUsed[page] != -1 else self.W[0]
elif page in self.Hist2:
pageevict = page
histpage_freq = self.Hist2.getCount(
page) ## Get the page frequency in history
self.Hist2.delete(page)
policyUsed = 1
self.W[1] = self.W[1] * (
1 -
self.epsilon) if self.policyUsed[page] != -1 else self.W[1]
self.W = self.W / np.sum(self.W)
# if pageevict is not None :
# q = self.weightsUsed[pageevict]
# # err = self.error_discount_rate ** (self.time - self.evictionTime[pageevict])
# err = 1
# reward = np.array([0,0], dtype=np.float32)
# if policyUsed == 0 : # LRU
# reward[0] = err
# if policyUsed == 1:
# reward[1] = err
# reward_hat = reward
#################
## Update Weights
#################
# if self.policyUsed[pageevict] != -1 :
# # self.W = self.W * np.exp(self.lamb * reward_hat / 2)
# self.W = self.W * (1 - reward*self.epsilon)
# self.W = self.W / np.sum(self.W)
####################
## Remove from Cache
####################
if self.CacheRecency.size() == self.N:
################
## Choose Policy
################
# if not self.learning :
# act = np.argmax(self.getQ())
# else :
# act = self.chooseRandom()
act = np.argmax(self.W) ## REMOVE
cacheevict, poly = self.selectEvictPage(act)
pagefreq = self.CacheFrequecy.getCount(cacheevict)
self.policyUsed[cacheevict] = poly
self.weightsUsed[cacheevict] = self.getQ()
self.evictionTime[cacheevict] = self.time
## TODO ADD BACK
# if not self.learning :
# self.policyUsed[cacheevict] = -1
###################
## Evict to history
###################
histevict = None
if (poly == 0) or (poly == -1 and np.random.rand() < 0.5):
if self.Hist1.size() == self.N:
histevict = self.Hist1.getIthPage(0)
self.Hist1.delete(histevict)
self.Hist1.add(cacheevict)
self.Hist1.setCount(cacheevict, pagefreq)
else:
if self.Hist2.size() == self.N:
histevict = self.Hist2.popmin()
self.Hist2.add(cacheevict)
self.Hist2.increase(cacheevict, pagefreq - 1)
if histevict is not None:
del self.evictionTime[histevict]
del self.policyUsed[histevict]
del self.weightsUsed[histevict]
self.evictPage(cacheevict)
self.addToCache(page, pagefreq=histpage_freq)
page_fault = True
return page_fault
def get_list_labels(self):
return ['L']
class LaCReME_simple(page_replacement_algorithm):
def __init__(self, N):
self.N = N
self.CacheRecency = Disk(N)
self.CacheFrequecy = priorityqueue(N)
self.Hist1 = Disk(N)
self.Hist2 = priorityqueue(N)
## Config variables
self.decayRate = 1
self.epsilon = 0.05
self.learning_phase = N / 2
##
self.learning = True
self.policy = 0
self.evictionTime = {}
self.policyUsed = {}
self.weightsUsed = {}
self.freq = {}
## TODO add decay_time and decay_factor
self.decay_time = N
self.decay_factor = 1
## Accounting variables
self.time = 0
self.W = np.array([.5, .5], dtype=np.float32)
## For visualization
self.X = np.array([], dtype=np.int32)
self.Y1 = np.array([])
self.Y2 = np.array([])
def get_N(self):
return self.N
def visualize(self, plt):
ax = plt.subplot(2, 1, 1)
ax.set_xlim(np.min(self.X), np.max(self.X))
l1, = plt.plot(self.X, self.Y1, 'b-', label='W_lru')
l2, = plt.plot(self.X, self.Y2, 'r-', label='W_lfu')
return [l1, l2]
##########################################
## Add a page to cache using policy 'poly'
##########################################
def addToCache(self, page, pagefreq=0):
self.CacheRecency.add(page)
self.CacheFrequecy.add(page)
self.CacheRecency.increaseCount(page, amount=pagefreq)
self.CacheFrequecy.increase(page, amount=pagefreq)
######################
## Get LFU or LFU page
######################
def selectEvictPage(self, policy):
r = self.CacheRecency.getIthPage(0)
f = self.CacheFrequecy.peaktop()
pageToEvit, policyUsed = None, None
if r == f:
pageToEvit, policyUsed = r, -1
elif policy == 0:
pageToEvit, policyUsed = r, 0
elif policy == 1:
pageToEvit, policyUsed = f, 1
return pageToEvit, policyUsed
def evictPage(self, pg):
self.CacheRecency.delete(pg)
self.CacheFrequecy.delete(pg)
##############################################################
## There was a page hit to 'page'. Update the data structures
##############################################################
def pageHitUpdate(self, page):
if page in self.CacheRecency and page in self.CacheFrequecy:
self.CacheRecency.moveBack(page)
self.CacheRecency.increaseCount(page)
self.CacheFrequecy.increase(page)
############################################
## Choose a page based on the q distribution
############################################
def chooseRandom(self):
q = self.getQ()
r = np.random.rand()
for i, p in enumerate(q):
if r < p:
return i
return len(q) - 1
##################
####REQUEST#######
##################
def request(self, page):
page_fault = False
self.time = self.time + 1
#####################
## Visualization data
#####################
self.X = np.append(self.X, self.time)
self.Y1 = np.append(self.Y1, self.W[0])
self.Y2 = np.append(self.Y2, self.W[1])
if self.time % self.N == 0:
self.CacheFrequecy.decay(self.decay_factor)
self.Hist2.decay(self.decay_factor)
##########################
## Process page request
##########################
if page in self.CacheFrequecy:
page_fault = False
self.pageHitUpdate(page)
else:
#####################################################
## Learning step: If there is a page fault in history
#####################################################
histpage_freq = 1
if page in self.Hist1:
histpage_freq = self.Hist1.getCount(page)
self.Hist1.delete(page)
self.W[0] = self.W[0] * (
1 -
self.epsilon) if self.policyUsed[page] != -1 else self.W[0]
elif page in self.Hist2:
histpage_freq = self.Hist2.getCount(
page) ## Get the page frequency in history
self.Hist2.delete(page)
self.W[1] = self.W[1] * (
1 -
self.epsilon) if self.policyUsed[page] != -1 else self.W[1]
####################
## Normalize weights
####################
self.W = self.W / np.sum(self.W)
####################
## Remove from Cache
####################
if self.CacheRecency.size() == self.N:
################################################################
## Choose Policy by picking the one with the highest weight
################################################################
act = np.argmax(self.W)
cacheevict, poly = self.selectEvictPage(act)
pagefreq = self.CacheFrequecy.getCount(cacheevict)
self.policyUsed[cacheevict] = poly
self.weightsUsed[cacheevict] = self.W
self.evictionTime[cacheevict] = self.time
###################
## Evict to history
###################
histevict = None
if (poly == 0) or (poly == -1 and np.random.rand() < 0.5):
if self.Hist1.size() == self.N:
histevict = self.Hist1.getIthPage(0)
self.Hist1.delete(histevict)
self.Hist1.add(cacheevict)
self.Hist1.setCount(cacheevict, pagefreq)
else:
if self.Hist2.size() == self.N:
histevict = self.Hist2.popmin()
self.Hist2.add(cacheevict)
self.Hist2.increase(cacheevict, pagefreq - 1)
##################################
## Remove histevict from the system
###################################
if histevict is not None:
del self.evictionTime[histevict]
del self.policyUsed[histevict]
del self.weightsUsed[histevict]
##############################################
## Remove cacheevict from both data structures
##############################################
self.evictPage(cacheevict)
###################################
## Add page to both data structures
###################################
self.addToCache(page, pagefreq=histpage_freq)
page_fault = True
return page_fault
def get_list_labels(self):
return ['L']