Cloud Computing is a new emerging paradigm that aims at streamlining the on-demand provisioning of resources as services, providing end-user with flexible and scalable services accessible through the Internet on a pay-per-use basis. Since modern Cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for Cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of SaaS (Software as a Service) providers which want to minimize the costs associated with the virtual machine instances allocated in a multi-IaaSs (Infrastructure as a Service) scenario, while avoiding incurring in penalties for requests execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, while the IaaSs want to maximize their revenues obtained providing virtualized resources.We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers 8-15% cost savings can be achieved from the workload distribution on multiple IaaSs.