Exploring the Potential of Elastic Computing Clusters in Geo-Distributed Data Centers with Fast Fabric Interconnection

Abstract

Large-scale enterprise computing systems are growing rapidly, to address the increasing demand for data processing; however, in many cases, the computing resources in a single data center may not be sufficient for critical data-centric workloads, and important factors, such as space limitations, power availability, or company policies, limit the possibilities of expanding the data center's resources. In this paper, we explore the potential of harvesting spare computing resources across geo-distributed data centers with fast fabric interconnection for real-world enterprise applications. We specifically characterize the computing resource utilization of four large-scale production data centers, and we show how to efficiently combine local storage and computing clusters with remote and elastic computation resources. The primary challenge is incorporating the available remote computing resources efficiently. To achieve this goal, we propose leveraging the capabilities of Kubernetes-based elastic computing clusters to utilize the spare computing resources across geo-distributed data centers for Big Data applications. We also provide an experimental performance evaluation based on real-use case scenarios via an empirical execution and a simulation, which shows that the proposed system can accelerate Big Data services by employing existing computing resources more efficiently across geo-distributed data centers.