بهینه سازی استراتژی های توسعه برای مراکز داده رایانش ابر ultrascale

With the increasing popularity gained by cloud computing systems over the past few years, cloud providers have built several ultrascale data centers at a variety of geographical locations, each including hundreds of thousands of computing servers. Since cloud providers are facing rapidly increasing traffic loads, they must have proper expansion strategies for their ultrascale data centers. The decision of expanding the capacities of existing data centers or building new ones over a certain period requires considering many factors, such as high power consumption, availability of resources, prices (of power, land, etc.), carbon tax, free cooling options, and availability of local renewable power generation. While a rich volume of recent research works focused on reducing the operational cost (OPEX) of the data centers, there exists no prior work, to the best of our knowledge, on investigating the trade-off between minimizing the OPEX of the data centers and maximizing their revenue from the services they offer while respecting the service level agreement (SLA) with their customers. In this study, we model this optimization problem using mixed integer-linear programming. Our proposed model is unique compared to the published works in many aspects such as its ability to handle realistic scenarios in which both data centers’ resources (servers) and user generated traffic are heterogeneous. To evaluate the proposed model and the impact of different parameters on it’s performance, several simulation experiments are conducted.