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

数据中心由商用服务器机架组成，通过多条高带宽链路相互连接。传输控制协议（TCP）是可靠传输数据的实际协议，并不适合数据中心；它是单路径的，它的拥塞控制算法，它调节发送者，使网络拥塞最小化，同时有效和公平地利用资源，遵循一个通用的方法。另一方面，数据中心支持服务器之间的高速、低延迟链路和多路径，以及具有不同数据传输需求的应用程序和不断变化的网络工作负载。该项目旨在利用机器学习技术，如贝叶斯优化和高斯过程，现代图形处理单元（gpu）提供的并行化能力，以及软件定义网络（SDN）来自动化控制拥塞和调度流的过程，以便即使在动态变化的网络流量下，数据中心网络资源也能得到有效利用。

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.