Automated holistic efficiency for next generation data centres

下一代数据中心的自动化整体效率

• 批准号: MR/T04389X/1
• 负责人: Stephen Clement
• 金额: $ 103.91万
• 依托单位: Edgetic Ltd
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT04389X%2F1
• 关键词: Automated; holistic; efficiency; next; generation

Data centres (DCs) provide critical infrastructure underpinning modern economies; they hold the software and data that modern life depends on. These facilities use massive amounts of power; from a few kilowatts up to hundreds of megawatts, much of it being generated in traditional carbon producing power stations. Although there is an increasing trend towards green energy supply, the need to improve DC efficiency as a whole is critical to UK industry. To date, most of the effort has focussed on physical systems. This project will radically improve current approaches by greatly expanding on the methodologies used model and simulate servers. We will introduce a step-change to DC efficiency by creating a holistic framework that accounts for software, hardware, facility, and human behaviours and use it in training advanced intelligent agents to achieve substantial energy reductions without affecting performance.The number and size of DCs is growing rapidly as they are the backbone of emerging technologies like IoT, 5G, AI, etc. Current DC energy usage is approximately 3% of global consumption; but could reach as high as 10% by 2030. To remain competitive in this growing sector it is vital that UK DCs keep their energy usage in check to compete with regions where green power is cheap. DC efficiency is driven by a number of factors: reducing carbon emissions, reducing costs and increasing capability where power is restricted. Edgetic is an early stage technology company aiming to improve DC efficiency via software services.The standard measure of DC efficiency is PUE (Power Utilisation Effectiveness): a ratio of the power consumed by the whole facility to that consumed by the IT equipment. A PUE of 1 is a theoretical minimum implying energy is only used by the IT hardware; efficiency worsens as PUE increases. Focusing on PUE, the industry has prioritised improving isolated peripheral systems rather than reducing overall energy consumption. PUE improvements are slowing as peripheral, co-dependant systems reach the limits of individual optimisation; improving IT efficiency is the next research frontier. Edgetic uses predictive mathematical modes of IT behaviour to make optimising decisions for the DC. However, our current approach requires individually modelling each workload and type of server in a DC. At present this is acceptable, but in order to substantially grow the business it is vital to improve the scalability of the modelling process since every DC is unique. Every additional variation in hardware and workload substantially increases the required evaluation. The aim of this project is to develop novel methods to speed up server evaluation, estimate behaviours of new hardware combinations and predict performance for different workloads. Uniquely, these methods will be employed in both the existing optimisation technology and provide the foundation for new artificial intelligence tools to optimise DC operation using holistic behaviour simulations. The holistic approach will allow automatic DC optimisation using new operating strategies tailored to individual DCs based on their required characteristics. This has the benefit of radically improving data centre efficiency which in turn reduces the climate impact of DCs and maintains the UK's leading position in the data centre industry.

数据中心提供支撑现代经济的关键基础设施；它们拥有现代生活所依赖的软件和数据。这些设施使用大量的电力；从几千瓦到数百兆瓦，其中大部分是由传统的碳发电站产生的。尽管绿色能源供应的趋势越来越大，但整体上提高直流效率的需求对英国工业至关重要。到目前为止，大部分工作都集中在物理系统上。该项目将通过极大地扩展所使用的建模和模拟服务器的方法，从根本上改进当前的方法。我们将通过创建一个考虑软件、硬件、设施和人类行为的整体框架，逐步提高直流效率，并将其用于培训高级智能代理，在不影响性能的情况下实现大幅节能。数据中心的数量和规模正在迅速增长，因为它们是物联网、5G、人工智能等新兴技术的支柱。目前的直流能源使用量约占全球能耗的3%；但到2030年，这一比例可能高达10%。为了在这个不断增长的领域保持竞争力，英国数据中心必须控制其能源使用，以与绿色电力便宜的地区竞争。直流电效率是由许多因素驱动的：减少碳排放，降低成本和增加功率受限的能力。Edgetic是一家早期技术公司，旨在通过软件服务提高直流效率。直流效率的标准度量是PUE（功率利用效率）：整个设施消耗的功率与IT设备消耗的功率之比。PUE为1是理论最小值，意味着能量仅由IT硬件使用；效率随着PUE的增加而恶化。专注于PUE，行业优先考虑的是改善隔离的外围系统，而不是降低整体能耗。随着外围、相互依赖的系统达到单个优化的极限，PUE的改进正在放缓；提高信息技术效率是下一个研究前沿。Edgetic使用IT行为的预测数学模式为DC做出优化决策。但是，我们当前的方法需要对数据中心中的每个工作负载和服务器类型单独建模。目前这是可以接受的，但为了大幅增长业务，提高建模过程的可扩展性至关重要，因为每个DC都是独一无二的。硬件和工作负载的每一个额外变化都会大大增加所需的评估。该项目的目的是开发新的方法来加快服务器评估，估计新硬件组合的行为，并预测不同工作负载的性能。独特的是，这些方法将用于现有的优化技术，并为新的人工智能工具提供基础，以利用整体行为模拟来优化直流操作。整体方法将允许使用基于所需特性的针对单个数据中心量身定制的新操作策略来自动优化数据中心。这样做的好处是从根本上提高数据中心的效率，从而减少数据中心的气候影响，并保持英国在数据中心行业的领先地位。