# 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)