Workload Based Geo-Distributed Data Center Planning in Fast Developing Economies

Abstract

Data centers (DCs), as the core infrastructure of cloud computing, are experiencing explosive growth worldwide. The planning of geo-distributed DCs has to take into account geographically differentiated costs, so that the overall expenditure could be minimized. In fast developing economies, the planning of DCs is further complicated by the fact that demands are quickly evolving, both in the type of services, and in geographical distributions. This paper is dedicated to the planning of DCs in fast developing economies. Instead of modeling demands by aggregated number of servers, we introduce a workload-based model to better capture the quickly changing nature of demand composition. The variability and dynamics of computational demand are modeled by dynamic compositions of workflows of different types. We provide approaches to solve the planning problem. Finally, we apply the model to China by using real-life data, and show how factors like economic growth, population migration and latency constraint may affect DC planning. Our research suggests that factors endemic in fast developing economies can significantly influence the overall cost and performance. For instance, by considering these factors, one can save 5.8% in cost in a five-year planning period, and 5% population migration may lead to 30ms increase in latency.