Cost Minimization of Service Deployment in a Public Cloud Environment

Public cloud computing allows one to rent virtual servers on a hourly basis. This raises the problematic of being able to decide which server offers to take, and how to use them to acquire sufficient service capacity, while maintaining a cost effective platform. This article proposes a new realistic model to tackle the problem, placing services into IAAS virtual machines. A flexible protocol is given to generate real-life instances, and applied on two industrial cases. An evolutionary approach, with new specific operators, is introduced and compared to a MIP formulation. Experimentations conducted on two datasets show that the evolutionary approach is viable to tackle real-size instances in reasonable amount of time.