Cost-efficient resource scheduling under QoS constraints for geo-distributed data centers

Geo-distributed Data Centers (DCs) are increasingly common in order to provide scalability for increasing compute demands of modern applications. When multiple geo-distributed DCs serve user requests, it is important to determine which DC and server to select to fulfill the demand at minimum cost, given that enough resources are available in terms of e.g., CPU and bandwidth. This is a complex task since every DC has different operational costs due to e.g. energy, carbon emission, and bandwidth costs. In this paper, we develop a novel mathematical optimization model that guides the decision maker which DC to select, which server to use, and which DC gateway and network path to use to route the user demand in order to satisfy the time varying compute, bandwidth, and latency demands. Our model is based on the concept of virtual networks, which have different requirements in terms of e.g. latency, and we model the queuing delay as a function of the traffic load. Our extensive numerical evaluation, which is based on real-world DC locations, their resource provision costs, and typical demand patterns, shows how operational costs increase with the traffic load, and we analyze the impact of different latency bounds on the performance of different virtual networks.