# network (GB/month) and storage (avg GB/month)
storage_used = generate_random_usage(40, 'spikes')
network_used_in = generate_random_usage(2, 'spikes')
network_used_out = generate_random_usage(20, 'spikes')
# consumed VM time (h/month) and used memory (GB/h*month), normalized to AWS CPU units
cpu_usage = generate_random_usage(5000, 'spikes')
mem_usage = generate_random_usage(12000, 'spikes')
# Perform basic analysis
from sitio.analyser import aws, rackspace
# Calculate storage cost on two clouds: AWS and Rackspace
# Pricelists in csv format are located in sitio/analyser/pricelist folder.
ebs_storage_cost, s3_storage_cost = aws.get_storage_costs(storage_used)
rack_storage_costs = rackspace.get_storage_costs(storage_used)
print "Storage costs on AWS: $%s, $%s" %(ebs_storage_cost, s3_storage_cost)
print "Storage costs on Rackspace: $%s" % rack_storage_costs
# To calculate migration costs in a straightforward manner - simply migrating all the data -
# we need to know two things: cost of moving out and cost of moving in.
aws_to_rack_migration_cost = aws.get_network_out_price(storage_used) + \
rackspace.get_network_in_price(storage_used)
print "Cost of migrating data from AWS to Rackspace: $%s " % aws_to_rack_migration_cost
# To get an estimate of how much a certain computational load would cost on a cloud,
# we provide an implementation of the method described in
# ''Towards a model for cloud computing cost estimation with reserved resources'', CLOUDCOMP'2010.
# It works by finding cheapest fit using LP of VM time and RAM consumption to cloud provider
# offering. The last boolean argument of the function signifies whether precise solution
# is sought (True, results in solving NP problem) or approximate is enough (False, fast).
def testAWSStorageCosts(self):
current_storage = generate_random_usage(random.randint(10,100), 'spikes')
rack_costs = rackspace.get_storage_costs(current_storage)
self.assertTrue(rack_costs > 0)
rack_costs = rackspace.get_storage_costs([0])
self.assertTrue(rack_costs == 0)
