Rethinking Data Center Networks: Machine Learning Enables Network Intelligence

Abstract

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. Machine learning (ML) provides an effective way to deal with these challenges by enabling network intelligence. To this end, numerous creative ML-based approaches have been put forward in recent years. Nevertheless, the intelligent optimization of data center networks (DCN) still faces enormous challenges. To the best of our knowledge, there is a lack of systematic and original investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we investigate the application of ML to DCN optimization and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, and resource management. Moreover, we also give unique insights into the technology evolution of the fusion of DCN and ML, together with some challenges and future research opportunities.