Machine Learning-Driven Generation of Congestion Control and Flow Scheduling Algorithms for Improving Data Centre Performance

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Data centres consist of racks of commodity servers interconnected by multiple, high bandwidth links. The Transmission Control Protocol (TCP), the de-facto protocol for reliably transmitting data, is not appropriate for data centres; it is single-path and its congestion control algorithm, which regulates senders so that network congestion is minimised, while resources are utilised efficiently and fairly, follows a one-size-fits-all approach.On the other hand, data centres support high-speed, low-latency links and multiple paths among servers as well as applications with diverse data transport requirements and network workloads which constantly change. This project aims at utilising machine learning techniques, such Bayesian optimisation and Gaussian processes, the power of parallelisation offered by modern Graphics Processing Units (GPUs), and Software Defined Networking (SDN) to automate the process of controlling congestion and scheduling flows so that data centre network resources are utilised efficiently even under dynamically changing network traffic.