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)
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))
