Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Abstract

Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components such as servers and switches/routers that work together to achieve a common objective and serve these applications. Managing such large data centers is a tedious process and demands automation, intelligent control and decision making within the data center. Recently both the industry and academia have focused on bringing intelligence to the control, automation, and management of DCNs. Despite the variety of works that surveyed ML for networking, to the best of our knowledge, none has focused on DCN, which makes this survey original. Readers in the academic and industrial communities will all benefit from a comprehensive discussion of the ML solutions applied in DCN to address critical essential problems, including workload forecasting, traffic flow control, traffic classification and scheduling, topology management, network state prediction, root cause analysis, and network security. Furthermore, this article outlines the challenges and concludes with the future research venues in adopting ML for automatic, intelligent and autonomous DCNs.

Keywords

• Data center network
• machine learning applications
• survey