  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.
To address these challenges, both academia and industry turn to artificial
intelligence technology to realize network intelligence. To this end, a
considerable number of novel and creative machine learning-based (ML-based)
research works have been put forward in recent few years. Nevertheless, there
are still enormous challenges faced by the intelligent optimization of data
center networks (DCNs), especially in the scenario of online real-time dynamic
processing of massive heterogeneous services and traffic data. To best of our
knowledge, there is a lack of systematic and original comprehensively
investigations with in-depth analysis on intelligent DCN. To this end, in this
paper, we comprehensively investigate the application of machine learning to
data center networking, and provide a general overview and in-depth analysis of
the recent works, covering flow prediction, flow classification, load
balancing, resource management, routing optimization, and congestion control.
In order to provide a multi-dimensional and multi-perspective comparison of
various solutions, we design a quality assessment criteria called REBEL-3S to
impartially measure the strengths and weaknesses of these research works.
Moreover, we also present unique insights into the technology evolution of the
fusion of data center network and machine learning, together with some
challenges and potential future research opportunities.
