def computeLambda(state, maximumTime=1.0):
activeAgents = state.getActiveAgents()
agentNames = getUniqueNames(activeAgents, prefix="Agent ")
classNames = getUniqueNames(state.getChoiceClasses(), prefix="Class ")
choiceNames = getUniqueNames(state.getChoices(), prefix="")
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0)
lambdaVariable = LpVariable("l", lowBound=0.0, upBound=maximumTime)
def createConstraints(problem, variable):
problem += lpSum(choiceVariables) <= 1, "Distribution"
for choiceClass, height in state.getCurrentClassHeights().items():
problem += createLpSum(choiceClass, choiceNames, choiceVariables) >= \
height, classNames[choiceClass] + " height"
for agent in state.getActiveAgents():
problem += createLpSum(state.getCurrentAgentChoiceClass(agent),
choiceNames, choiceVariables) >= \
state.getAgentHeight(agent) + variable * state.getAgentSpeed(agent), \
agentNames[agent] + " push"
problem = LpProblem("Lambda", LpMaximize)
createConstraints(problem, lambdaVariable)
problem.setObjective(lambdaVariable)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
lambdaOpt = lambdaVariable.value()
bouncingAgents = []
stringAgents = [] #TODO just a workaround; find better solution
for currentAgent in activeAgents:
print "Current agent: " + agentNames[
currentAgent] # name is off by one
problem = LpProblem(agentNames[currentAgent], LpMaximize)
createConstraints(problem, lambdaOpt)
(choiceClass, height, speed) = state.getAgentData(currentAgent)
# print "Choice Class: " + repr(choiceClass) + ",
print "height: " + repr(height) + " , speed: " + repr(speed)
problem.setObjective(
createLpSum(choiceClass, choiceNames, choiceVariables) -
lambdaOpt * speed - height)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
value = problem.objective.value()
# print "value: " + repr(value) + "\n"
if not state.getSettings().isNonnegative(value):
raise ValueError(
str(value) + " negative while determining bounce of " +
repr(currentAgent) + " from " + repr(choiceClass) + "@" +
str(height) + "/" + str(speed))
if state.getSettings().isClose(value, 0):
bouncingAgents.append(currentAgent)
stringAgents.append(int(currentAgent.getName()))
print "climbing time: " + repr(lambdaOpt) + ", bouncingAgents: " + str(
stringAgents) + "\n"
# print map(Agent.getName(), bouncingAgents)
return (lambdaOpt, bouncingAgents)
def computeLambda(state, maximumTime=1.0):
activeAgents = state.getActiveAgents()
agentNames = getUniqueNames(activeAgents, prefix="Agent ")
classNames = getUniqueNames(state.getChoiceClasses(), prefix="Class ")
choiceNames = getUniqueNames(state.getChoices(), prefix="")
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0)
lambdaVariable = LpVariable("l", lowBound=0.0, upBound=maximumTime)
def createConstraints(problem, variable):
problem += lpSum(choiceVariables) <= 1, "Distribution"
for choiceClass, height in state.getCurrentClassHeights().items():
problem += createLpSum(choiceClass, choiceNames, choiceVariables) >= \
height, classNames[choiceClass] + " height"
for agent in state.getActiveAgents():
problem += createLpSum(state.getCurrentAgentChoiceClass(agent),
choiceNames, choiceVariables) >= \
state.getAgentHeight(agent) + variable * state.getAgentSpeed(agent), \
agentNames[agent] + " push"
problem = LpProblem("Lambda", LpMaximize)
createConstraints(problem, lambdaVariable)
problem.setObjective(lambdaVariable)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
lambdaOpt = lambdaVariable.value()
bouncingAgents = []
stringAgents = [] #TODO just a workaround; find better solution
for currentAgent in activeAgents:
print "Current agent: " + agentNames[currentAgent] # name is off by one
problem = LpProblem(agentNames[currentAgent], LpMaximize)
createConstraints(problem, lambdaOpt)
(choiceClass, height, speed) = state.getAgentData(currentAgent)
# print "Choice Class: " + repr(choiceClass) + ",
print "height: " + repr(height) + " , speed: " + repr(speed)
problem.setObjective(createLpSum(choiceClass, choiceNames, choiceVariables)
- lambdaOpt * speed - height)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
value = problem.objective.value()
# print "value: " + repr(value) + "\n"
if not state.getSettings().isNonnegative(value):
raise ValueError(str(value) + " negative while determining bounce of " +
repr(currentAgent) + " from " + repr(choiceClass) +
"@" + str(height) + "/" + str(speed))
if state.getSettings().isClose(value, 0):
bouncingAgents.append(currentAgent)
stringAgents.append(int(currentAgent.getName()))
print "climbing time: " + repr(lambdaOpt) + ", bouncingAgents: " + str(stringAgents) + "\n"
# print map(Agent.getName(), bouncingAgents)
return (lambdaOpt, bouncingAgents)
def findLottery(vote, classHeights, solverSettings):
'''
Returns a Lottery satisfying all constraints specified by the classHeights parameter
@type vote: vote.society.Vote
@type classHeights: dict(vote.society.ChoiceClass, float)
@type solverSettings: vote.solver.settings.SolverSettings
@rtype: vote.society.Lottery
@raise ValueError: If the constraints are not satisfiable
'''
classNames = getUniqueNames(classHeights.keys(), prefix="Class ")
choiceNames = getUniqueNames(vote.getChoices(), prefix="Choice ")
problem = LpProblem("Lambda", LpMaximize)
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0)
problem += lpSum(choiceVariables) <= 1, "Distribution"
for choiceClass, height in classHeights.items():
problem += createLpSum(choiceClass, choiceNames, choiceVariables) >= \
height, classNames[choiceClass] + " height"
problem.setObjective(lpSum(choiceVariables.values()))
checkPulpStatus(problem.solve(solverSettings.getSolver()))
choiceValues = dict()
for choice, choiceName in choiceNames.items():
choiceValues[choice.getObject()] = choiceVariables[choiceName].value()
return Lottery(choiceValues, solverSettings)
def findLottery(vote, classHeights, solverSettings):
'''
Returns a Lottery satisfying all constraints specified by the classHeights parameter
@type vote: vote.society.Vote
@type classHeights: dict(vote.society.ChoiceClass, float)
@type solverSettings: vote.solver.settings.SolverSettings
@rtype: vote.society.Lottery
@raise ValueError: If the constraints are not satisfiable
'''
classNames = getUniqueNames(classHeights.keys(), prefix="Class ")
choiceNames = getUniqueNames(vote.getChoices(), prefix="Choice ")
problem = LpProblem("Lambda", LpMaximize)
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0)
problem += lpSum(choiceVariables) <= 1, "Distribution"
for choiceClass, height in classHeights.items():
problem += createLpSum(choiceClass, choiceNames, choiceVariables) >= \
height, classNames[choiceClass] + " height"
problem.setObjective(lpSum(choiceVariables.values()))
checkPulpStatus(problem.solve(solverSettings.getSolver()))
choiceValues = dict()
for choice, choiceName in choiceNames.items():
choiceValues[choice.getObject()] = choiceVariables[choiceName].value()
return Lottery(choiceValues, solverSettings)
def genera_strutture_ausiliarie(self, dati) -> None:
model = pl.LpProblem("CalcolaOrario", pl.LpMaximize)
# set dei docenti che hanno insegnamenti tra i moduli da fissare in orario
doc = set(m.get_matricola() for m in dati.get_moduli())
# dict che mantiene l'associazione tra i moduli e i docenti titolari degli stessi
tit = LpVariable.dicts("assign", [(m,d) for m in dati.get_moduli() for d in doc],
lowBound = 0,
upBound = 1,
cat = pl.LpInteger)
# dict che mantiene l'associazione tra i moduli e le aule compatibili in relazione alla numerosità attesa
cpt = LpVariable.dicts("assign", [(m,a) for m in dati.get_moduli() for a in dati.get_aule()],
lowBound = 0,
upBound = 1,
cat = pl.LpInteger)
# valore 1 -> docente ha la titolarità di un dato modulo
for m in dati.get_moduli():
for d in doc:
if d == m.get_matricola():
tit[m,d] = 1
else:
tit[m,d] = 0
# valore 1 -> l'aula è compatibile con la numerosità di un dato corso
for m in dati.get_moduli():
for a in dati.get_aule():
if a.get_tipo() == m.get_tipo_aula() and a.get_capienza() >= m.get_max_studenti():
cpt[m,a] = 1
else:
cpt[m,a] = 0
# Le variabili skd valgono uno se per un dato slot di un dato giorno viene assegnata la lezione di un modulo
# di una dato corso in un data aula
skd = LpVariable.dicts("assign", [(c,m,a,g,s) for c in dati.get_corsi() for m in dati.get_moduli() for a in dati.get_aule()
for g in dati.get_giorni() for s in dati.get_slot()],
lowBound = 0,
upBound = 1,
cat = pl.LpInteger)
return model, StruttureAusiliarie(doc, tit, cpt, skd)
def computeLambda(state, maximumTime=1.0):
'''
@type state: SSRState
@type maximumTime: float
'''
towerNames = getUniqueNames(state.getTowers(), prefix="T")
choiceNames = getUniqueNames(state.getChoices(), prefix="")
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0.0)
lambdaVariable = LpVariable("l", lowBound=0.0, upBound=maximumTime)
def createConstraints(problem, variable):
problem += lpSum(choiceVariables) <= 1, "Distribution"
for tower, towerName in towerNames.items():
problem += createLpSum(tower.getChoiceClass(), choiceNames, choiceVariables) >= \
tower.getHeight() + variable * \
tower.getSpeed(), towerName
problem = LpProblem("Lambda", LpMaximize)
createConstraints(problem, lambdaVariable)
problem.setObjective(lambdaVariable)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
lambdaOpt = lambdaVariable.value()
freezingTowers = []
for currentTower, towerName in towerNames.items():
if currentTower.isFrozen():
continue
problem = LpProblem(towerName, LpMaximize)
createConstraints(problem, lambdaOpt)
problem.setObjective(
createLpSum(currentTower.getChoiceClass(), choiceNames,
choiceVariables) -
lambdaOpt * currentTower.getSpeed() - currentTower.getHeight())
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
value = problem.objective.value()
if not state.getSettings().isNonnegative(value):
raise ValueError(
str(value) + " negative while determining frozen state of " +
repr(currentTower))
if state.getSettings().isClose(problem.objective.value(), 0):
freezingTowers.append(currentTower)
return (lambdaOpt, frozenset(freezingTowers))
def computeLambda(state, maximumTime=1.0):
'''
@type state: SSRState
@type maximumTime: float
'''
towerNames = getUniqueNames(state.getTowers(), prefix="T")
choiceNames = getUniqueNames(state.getChoices(), prefix="")
choiceVariables = LpVariable.dicts("p", choiceNames.values(), lowBound=0.0)
lambdaVariable = LpVariable("l", lowBound=0.0, upBound=maximumTime)
def createConstraints(problem, variable):
problem += lpSum(choiceVariables) <= 1, "Distribution"
for tower, towerName in towerNames.items():
problem += createLpSum(tower.getChoiceClass(), choiceNames, choiceVariables) >= \
tower.getHeight() + variable * \
tower.getSpeed(), towerName
problem = LpProblem("Lambda", LpMaximize)
createConstraints(problem, lambdaVariable)
problem.setObjective(lambdaVariable)
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
lambdaOpt = lambdaVariable.value()
freezingTowers = []
for currentTower, towerName in towerNames.items():
if currentTower.isFrozen():
continue
problem = LpProblem(towerName, LpMaximize)
createConstraints(problem, lambdaOpt)
problem.setObjective(createLpSum(currentTower.getChoiceClass(), choiceNames, choiceVariables)
- lambdaOpt * currentTower.getSpeed() - currentTower.getHeight())
checkPulpStatus(problem.solve(state.getSettings().getSolver()))
value = problem.objective.value()
if not state.getSettings().isNonnegative(value):
raise ValueError(str(value) + " negative while determining frozen state of " +
repr(currentTower))
if state.getSettings().isClose(problem.objective.value(), 0):
freezingTowers.append(currentTower)
return (lambdaOpt, frozenset(freezingTowers))
def get_optimal_rackspace(cpu_usage, mem_usage, optimal=True, debug=False):
"""
Calculates the price of optimal resources allocation over a certain time span (with monthly granularity).
Formulates the problem of satisfying user demand (in CPU and RAM) as an LP problem with a monetary objective function.
"""
assert(len(cpu_usage) == len(mem_usage))
prob = LpProblem("Rackspace cost optimization", LpMinimize)
# variables
## 1h instances
per_h_ondems = []
for p in range(len(cpu_usage)):
per_h_ondems += ["p %s ondem %s" %(p, i) for i in vms.keys()]
category = LpInteger if optimal else LpContinuous
vars = LpVariable.dicts("rackspace", per_h_ondems, 0, None, cat=category)
# objective function
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][0] for vm in per_h_ondems]), "Total cost of running the infra (wrt to CPU/RAM)"
# constraints
## demand constraints
for p in range(len(cpu_usage)):
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][2] for vm in per_h_ondems if int(vm.split(" ")[1]) == p]) >= cpu_usage[p], "CU demand period %s" %p
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][3] for vm in per_h_ondems if int(vm.split(" ")[1]) == p]) >= mem_usage[p], "RAM demand period %s" %p
prob.solve()
if debug:
for v in prob.variables():
if v.varValue != 0.0:
print v.name, "=", v.varValue
print "Total Cost of the solution = ", value(prob.objective)
return value(prob.objective)
}
AllStudentsNumber = {
"1": [53, 63, 65, 63, 94, 42, 58],
"2": [13, 53, 69, 24, 42, 19, 48],
"3": [40, 7, 10, 82, 64, 86, 2],
"4": [51, 19, 36, 75, 92, 92, 14],
"5": [73, 82, 22, 29, 72, 74, 77],
"6": [73, 82, 22, 29, 72, 74, 77],
}
# 今回の問題では総和を最大化するので LpMaximize を指定する
problem = LpProblem('GroupAllScore', LpMaximize)
# 使用する変数
s1 = LpVariable('S1', 1, 6, 'Continuous')
s2 = LpVariable('S2', 1, 6, 'Continuous')
s3 = LpVariable('S3', 1, 6, 'Continuous')
s4 = LpVariable('S4', 1, 6, 'Continuous')
s5 = LpVariable('S5', 1, 6, 'Continuous')
s6 = LpVariable('S6', 1, 6, 'Continuous')
group1 = [s1, s2]
group2 = [s3, s4]
group3 = [s5, s6]
AllStudentsNumberDup = group1 + group2 + group3
# 目的関数
# score1 = AllStudents[group1[0]][0] + AllStudents[group1[1]][1]
# score2 = AllStudents[group2[0]][0] + AllStudents[group2[1]][1]
# score3 = AllStudents[group3[0]][0] + AllStudents[group3[1]][1]
# problem += score1 + score2 + score3
from pulp.constants import LpMaximize
from pulp.pulp import LpProblem, LpVariable
# 今回の問題では総和を最大化するので LpMaximize を指定する
problem = LpProblem('Restaurant', LpMaximize)
# 使用する変数
x = LpVariable('X')
y = LpVariable('Y')
# 目的関数
problem += 30 * x + 25 * y
# 制約条件
problem += 0.5 * x + 0.25 * y <= 10 # ひき肉の総量は 3800g
problem += 0.5 * x + 0.75 * y <= 15 # 玉ねぎの総量は 2100g
# 解く
problem.solve()
# 結果表示
print('x: {x}'.format(x=x.value()))
print('y: {y}'.format(y=y.value()))
print(x.value() * y.value())
# class Node:
# def __init__(self, x, y):
# self.x = x
# self.y = y
# self.state = None
def lp_model(cg: ComputationGraph,
agentsdef: Iterable[AgentDef],
footprint: Callable[[str], float],
capacity: Callable[[str], float],
route: Callable[[str, str], float],
msg_load: Callable[[str, str], float],
hosting_cost: Callable[[str, str], float]):
comp_names = [n.name for n in cg.nodes]
agt_names = [a.name for a in agentsdef]
pb = LpProblem('ilp_compref', LpMinimize)
# One binary variable xij for each (variable, agent) couple
xs = LpVariable.dict('x', (comp_names, agt_names), cat=LpBinary)
# One binary variable for computations c1 and c2, and agent a1 and a2
betas = {}
count = 0
for a1, a2 in combinations(agt_names, 2):
# Only create variables for couple c1, c2 if there is an edge in the
# graph between these two computations.
for l in cg.links:
# As we support hypergraph, we may have more than 2 ends to a link
for c1, c2 in combinations(l.nodes, 2):
count += 2
b = LpVariable('b_{}_{}_{}_{}'.format(c1, a1, c2, a2),
cat=LpBinary)
betas[(c1, a1, c2, a2)] = b
pb += b <= xs[(c1, a1)]
pb += b <= xs[(c2, a2)]
pb += b >= xs[(c2, a2)] + xs[(c1, a1)] - 1
b = LpVariable('b_{}_{}_{}_{}'.format(c1, a2, c2, a1),
cat=LpBinary)
betas[(c1, a2, c2, a1)] = b
pb += b <= xs[(c2, a1)]
pb += b <= xs[(c1, a2)]
pb += b >= xs[(c1, a2)] + xs[(c2, a1)] - 1
# Set objective: communication + hosting_cost
pb += _objective(xs, betas, route, msg_load, hosting_cost), \
'Communication costs and prefs'
# Adding constraints:
# Constraints: Memory capacity for all agents.
for a in agt_names:
pb += lpSum([footprint(i) * xs[i, a] for i in comp_names])\
<= capacity(a), \
'Agent {} capacity'.format(a)
# Constraints: all computations must be hosted.
for c in comp_names:
pb += lpSum([xs[c, a] for a in agt_names]) == 1, \
'Computation {} hosted'.format(c)
# solve using GLPK
status = pb.solve(solver=GLPK_CMD(keepFiles=1, msg=False,
options=['--pcost']))
if status != LpStatusOptimal:
raise ImpossibleDistributionException("No possible optimal"
" distribution ")
logger.debug('GLPK cost : %s', value(pb.objective))
# print('BETAS:')
# for c1, a1, c2, a2 in betas:
# print(' ', c1, a1, c2, a2, value(betas[(c1, a1, c2, a2)]))
#
# print('XS:')
# for c, a in xs:
# print(' ', c, a, value(xs[(c, a)]))
mapping = {}
for k in agt_names:
agt_computations = [i for i, ka in xs
if ka == k and value(xs[(i, ka)]) == 1]
# print(k, ' -> ', agt_computations)
mapping[k] = agt_computations
return mapping
def distribute_factors(
agents: Dict[str, AgentDef],
cg: ComputationGraph,
footprints: Dict[str, float],
mapping: Dict[str, List[str]],
msg_load: Callable[[str, str], float],
) -> Dict[str, List[str]]:
"""
Optimal distribution of factors on agents.
Parameters
----------
cg: computations graph
agents: dict
a dict {agent_name : AgentDef} containing all available agents
Returns
-------
a dict { agent_name: list of factor names}
"""
pb = LpProblem("ilp_factors", LpMinimize)
# build the inverse mapping var -> agt
inverse_mapping = {} # type: Dict[str, str]
for a in mapping:
inverse_mapping[mapping[a][0]] = a
# One binary variable xij for each (variable, agent) couple
factor_names = [n.name for n in cg.nodes if isinstance(n, FactorComputationNode)]
xs = LpVariable.dict("x", (factor_names, agents), cat=LpBinary)
logger.debug("Binary variables for factor distribution : %s", xs)
# Hard constraints: respect agent's capacity
for a in agents:
# Footprint of the variable this agent is already hosting:
v_footprint = footprints[mapping[a][0]]
pb += (
lpSum([footprints[fn] * xs[fn, a] for fn in factor_names])
<= (agents[a].capacity - v_footprint),
"Agent {} capacity".format(a),
)
# Hard constraints: all computations must be hosted.
for c in factor_names:
pb += lpSum([xs[c, a] for a in agents]) == 1, "Factor {} hosted".format(c)
# 1st objective : minimize communication costs:
comm = LpAffineExpression()
for (fn, an_f) in xs:
for vn in cg.neighbors(fn):
an_v = inverse_mapping[vn] # agt hosting neighbor var vn
comm += agents[an_f].route(an_v) * msg_load(vn, fn) * xs[(fn, an_f)]
# 2st objective : minimize hosting costs
hosting = lpSum([agents[a].hosting_cost(c) * xs[(c, a)] for c, a in xs])
# agregate the two objectives using RATIO_HOST_COMM
pb += lpSum([RATIO_HOST_COMM * comm, (1 - RATIO_HOST_COMM) * hosting])
# solve using GLPK and convert to mapping { agt_name : [factors names]}
status = pb.solve(solver=GLPK_CMD(keepFiles=1, msg=False, options=["--pcost"]))
if status != LpStatusOptimal:
raise ImpossibleDistributionException(
"No possible optimal distribution for factors"
)
logger.debug("GLPK cost : %s", value(pb.objective))
mapping = {} # type: Dict[str, List[str]]
for k in agents:
agt_computations = [i for i, ka in xs if ka == k and value(xs[(i, ka)]) == 1]
# print(k, ' -> ', agt_computations)
mapping[k] = agt_computations
logger.debug("Factors distribution : %s ", mapping)
return mapping
def get_optimal_ec2(cpu_usage, mem_usage, optimal=True, debug=False):
"""
Calculates the optimal allocation of resources over a certain time span (with monthly granularity).
Formulates the problem of satisfying user demand (in CPU and RAM) as an LP problem with a monetary objective function.
Returns allocation of reserved instances and a total price for running the allocation on AWS.
"""
assert(len(cpu_usage) == len(mem_usage))
prob = LpProblem("The Simplified EC2 cost optimization", LpMinimize)
# variables
## 1h instances (both on-demand and reserved)
per_h_ondems = []
per_h_reserved = []
for p in range(len(cpu_usage)):
# ondemand
per_h_ondems += ["p %s ondem %s" %(p, i) for i in vms.keys()]
# reserved
per_h_reserved += ["p %s reserved %s" %(p, i) for i in vms.keys()]
## nr of 1-year reserved instances
nr_of_1year_reserved = [ "res_1year %s" % i for i in vms.keys()]
nr_of_3year_reserved = [ "res_3year %s" % i for i in vms.keys()]
category = LpInteger if optimal else LpContinuous
vars = LpVariable.dicts("aws", per_h_ondems + per_h_reserved + nr_of_1year_reserved + nr_of_3year_reserved, \
lowBound = 0, upBound = None, cat = category)
# objective function
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][0] for vm in per_h_ondems]) \
+ lpSum([vars[vm] * vms[vm.split(" ")[3]][3] for vm in per_h_reserved]) \
+ lpSum([vars[vm] * vms[vm.split(" ")[1]][1] for vm in nr_of_1year_reserved]) \
+ lpSum([vars[vm] * vms[vm.split(" ")[1]][2] for vm in nr_of_3year_reserved]) \
, "Total cost of running the infrastructure consuming (CPU/RAM)/h"
# constraints
## demand constraints
for p in range(len(cpu_usage)):
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][4] for vm in (per_h_ondems + per_h_reserved) if int(vm.split(" ")[1]) == p]) >= cpu_usage[p], "CPU demand, period %s" %p
prob += lpSum([vars[vm] * vms[vm.split(" ")[3]][5] for vm in (per_h_ondems + per_h_reserved) if int(vm.split(" ")[1]) == p]) >= mem_usage[p], "RAM demand. period %s" %p
## constraints on the reserved instances - cannot use more than we paid for
for i in per_h_reserved:
t = i.split(" ")[3]
prob += vars["res_1year %s" % t] + vars["res_3year %s" % t] >= vars[i], "Nr. of used reserved machines of type %s" %i
prob.solve()
if debug:
print "Status:", LpStatus[prob.status]
print "Total Cost of the solution = ", value(prob.objective)
res_instance = {}
for v in prob.variables():
if v.name.startswith("aws_res_") and v.varValue != 0.0:
res_instance[v.name] = v.varValue
if debug and v.varValue != 0.0:
print v.name, "=", v.varValue
return (res_instance, value(prob.objective))
