def adjustRate(routL, servrates, cusL, qTypeL, adjustIndex, maxError): #print "In adjustRate:" rateToAdjust = servrates[adjustIndex[0]][adjustIndex[1]] lamL, T = mva_multiclass(routL, servrates, cusL, qTypeL) v = 0.1 diff = lamL[0] - lamL[1] while math.fabs(diff) > maxError: #print "oldServrates:" #print servrates #print "lambda difference:" #print lamL[0] - lamL[1] if diff > 0: #(the writer is faster than the reader) rateToAdjust += v#increase the rate of the reader else: rateToAdjust -= v#decrease the rate of the reader #plug ratetoAdjust back to the rates servrates = updateRates(servrates, adjustIndex, rateToAdjust) lamL, T = mva_multiclass(routL, servrates, cusL, qTypeL) #print "newServrates:" #print servrates v = v/2 if diff == lamL[0] - lamL[1]: break diff = lamL[0] - lamL[1] return servrates
def runmepan(msg):
if msg != 'imhappy':
return 0
rout = [[0,1,0,0],[0,0,1,0],[0,0,0,1],[1,0,0,0]]
for x in range(10,11):
#print "service rate for the reader local computation: ",x
servrates = np.array([[1.0,1.0],[1.0,1.0],[1.0,x],[1.0,0.7]])
la,T = mva_multiclass([rout,rout], servrates, (2,2), [1,0,1,0])
print "lambda before adjusting rates"
print la[0],la[1]
print "The adjusted rates for "
print x
newRates = adjustRate([rout,rout], servrates, (2,2), [1,0,1,0], [3,1],la[0]/100.0)
print newRates
l,T = mva_multiclass([rout,rout], newRates, (2,2), [1,0,1,0])
print l[0],l[1]
def mva(self, pop_vector = None):
if not self.rout_l:
self.analyze(self)
res_tmp = mva_multiclass(self.rout_l, self.serv_rates, pop_vector or tuple(map(len, self.classes)), self.q_type)
self.est_wait = res_tmp[0][1::2]
self.est_qlen = res_tmp[1][1::2]
self.lam = res_tmp[2]
def bodytrack(threadstuple, serv):
#serv_M = ma.asarray([[0, 0, 0, 0, 6983544, 91465, 23131612, 171783],
# [340208, 21308, 230049, 18250, 83687, 35901, 0, 0],
# [0, 0, 0, 0, 0, 0, 22032093, 103155]])
serv_M = ma.asarray(serv)
rout = [
[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 1],
[0, 0, 1, 0]],
[[39, 1, 0, 0],
[0, 4, 1, 0],
[1, 0, 1, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]]
]
qt = list(islice(cycle((1,0)), serv_M.shape[1]))
routL = map (lambda x: rout_insert_inter(normalizeRowWise(np.asarray(x))), rout)
res = mva_multiclass (routL, 1./serv_M.T, threadstuple, qt)
cont = (res[0][1::2] - serv_M.T[1::2]).T
print "%.0f %.0f %.0f" % (cont[1,0], cont[0,2], cont[2,3])
return res
def streamcluster(threadstuple):
#serv_M = ma.asarray([[248722, 45232],[350726, 60236]])
#serv_M = ma.asarray([[2514000, 151000],[3500000, 300000]])
serv_M = ma.asarray([[993824, 60288, ],
[1397572, 60455, ],
])
rout = [[[1]],
[[1]]]
qt = list(islice(cycle((1,0)), serv_M.shape[1]))
routL = map (lambda x: rout_insert_inter(normalizeRowWise(np.asarray(x))), rout)
res = mva_multiclass (routL, 1./serv_M.T, threadstuple, qt)
print (res[0][1::2] - serv_M.T[1::2]).T
return res
def parse_and_run_mva (filename, nclass):
'''parse service times from micro benchmark with one lock
'''
nlocks = 1
nthreads = 0
if nclass > 1:
serv_vec = [[],[]]
wait_vec = [[],[]]
else:
serv_vec = [[]]
wait_vec = [[]]
with open(filename) as f:
recReader = csv.reader (f, delimiter=' ', skipinitialspace=True)
for _row in recReader:
tid = int(_row[0])
think_time = float(_row[2])
q_time = float(_row[3])
serv_time = float(_row[4])
serv_vec[tid % nclass].append([think_time, serv_time])
wait_vec[tid % nclass].append(serv_time+q_time)
nthreads += 1
n_per_c = nthreads
arr = np.asarray(serv_vec)
serv = np.average(arr, axis=1)
rout = np.eye(2)
routL = list(repeat(rout, nclass))
qt = list(islice(cycle((1,0)), nlocks*2))
serv_M = listOfArrToMtx(serv)
res = mva_multiclass (routL, 1./serv_M.T, tuple([n_per_c]*nclass), qt)
est_wait = res[0][1]
meas_wait = np.average(np.array(wait_vec), axis=1)
for i in xrange(nclass):
print "#CLASS NTHS MEAS_S EST_W MEAS_W OFFSET MEAS_TH"
print "%d %d %.0f %.0f %.0f %.0f %.0f" % (i, nthreads, serv_M.T[1][i], est_wait[i], meas_wait[i], meas_wait[i]-est_wait[i], serv_M.T[0][i])
return res
