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

机器学习驱动的拥塞控制和流量调度算法的生成，用于提高数据中心性能

• 批准号: 1804241
• 金额: --
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1804241
• 关键词: Machine; Learning; Driven; Generation; Congestion

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.

数据中心由通过多个高带宽链路互连的商品服务器机架组成。传输控制协议（TCP）是可靠传输数据的事实协议，不适合数据中心;它是单路径的，其拥塞控制算法遵循一种一刀切的方法，该算法调节拥塞，以最大限度地减少网络拥塞，同时有效和公平地利用资源。另一方面，数据中心支持高速，服务器之间的低延迟链路和多路径以及具有不同数据传输要求和不断变化的网络工作负载的应用程序。 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）提供的并行化功能，以及软件定义网络（SDN）来自动控制拥塞和调度流程，以便即使在动态变化的网络流量下也能有效利用数据中心网络资源。