Revenue Maximization for Dynamic Expansion of Geo-distributed Cloud Data Centers

In the cloud environment, it brings better reliability and robustness with geographically distributed datacenters. As the growth of large-scale applications in geo-distributed cloud systems, the resource demand from different areas increases violently, and researchers pay more attention to meet as many cloud users' VM demands as possible by using limited cloud resources. However, there exist many issues for cloud users in existing works, such as the VM demands being refused and high response latency. In this paper, we present a cloud system model for the cloud provider to dynamically expand the scale of geo-distributed date centers. In our model, the cloud provider rents hardware resources from other resource owners (ROs), who have redundant resources and are willing to lease them. Since the ROs possess vast resources and spread all over the global, our system model can deploy more cloud users' VMs and effectively reduce the bandwidth cost. We propose an optimization problem for the cloud provider to maximize the profit, and carefully solve it in different conditions. Our simulation results show that our system model and algorithms can effectively improve the user satisfaction and the total revenue and reduce the average latency of users' requests.