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/2
• 负责人: Stephen Clement
• 金额: $ 98.05万
• 依托单位: Techbuyer Limited
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT04389X%2F2
• 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（DC）是支撑现代经济的关键基础设施，拥有现代生活所依赖的软件和数据。这些设施使用大量电力，从几千瓦到数百兆瓦，其中大部分来自传统的碳排放发电站。尽管绿色能源供应的趋势日益明显，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模型和模拟服务器所使用的方法，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来逐步改变DC效率，并将其用于培训高级智能代理，以在不影响性能的情况下实现大幅节能。DC的数量和规模正在迅速增长，因为它们是物联网、5G、AI、目前，DC能源使用量约占全球消费量的3%;但到2030年可能高达10%。为了在这一不断增长的行业保持竞争力，英国发展中国家必须控制其能源使用，以与绿色电力便宜的地区竞争。直流电效率受多个因素影响：减少碳排放、降低成本以及在电力受限的情况下提高容量。Edgetic是一家早期的技术公司，旨在通过软件服务提高DC效率。DC效率的标准衡量标准是PUE（电力利用效率）：整个设施消耗的电力与IT设备消耗的电力的比率。PUE为1是理论上的最小值，这意味着能量仅由IT硬件使用;效率随着PUE的增加而增加。专注于PUE，该行业优先考虑改善隔离的外围系统，而不是降低整体能耗。PUE的改进正在放缓，因为外围设备，相互依赖的系统达到了个人优化的极限;提高IT效率是下一个研究前沿。Edgetic使用IT行为的预测数学模型为DC做出优化决策。然而，我们目前的方法需要单独建模DC中的每个工作负载和服务器类型。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都是唯一的。硬件和工作负载中的每一个额外变化都大大增加了所需的评估。该项目的目的是开发新的方法来加速服务器评估，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，以使用整体行为模拟来优化DC操作。整体方法将允许自动DC优化，使用基于其所需特性针对单个DC量身定制的新操作策略。这有利于从根本上提高数据中心的效率，从而减少发展中国家对气候的影响，并保持英国在数据中心行业的领先地位。

项目成果

Maximising Server IT Energy Efficiency through Optimal Hardware and Software Configuration

通过优化硬件和软件配置最大限度地提高服务器 IT 能源效率

• DOI: 10.1109/icac57885.2023.10275260
• 发表时间: 2023
• 作者: Burdett K

Is Hot IT a False Economy? An Analysis of Server and Data Center Energy Efficiency as Temperatures Rise

热门IT是虚假经济吗？

• DOI: 10.1109/tsusc.2023.3336801
• 发表时间: 2023
• 期刊: IEEE Transactions on Sustainable Computing
• 影响因子: 3.9
• 作者: Clement S

Balancing Power and Performance: A Multi-Generational Analysis of Enterprise Server BIOS Profiles

平衡功耗和性能：企业服务器 BIOS 配置文件的多代分析

• DOI: 10.1109/gecost55694.2022.10010599
• 发表时间: 2022
• 作者: Rteil N