Alibaba Innovative Research (AIR) > Research on Frontier Technologies in Data Center and Server
【CCF-AIR青年基金】Research on Coordinated Optimization of Internet Data Center Computing Power Based on Elastic Scheduling of Workload

Research Themes

Research on Frontier Technologies in Data Center and Server

Background

With the continuing growth of the digital economy and the steady promotion of New Infrastructure Strategy, internet data centers (IDCs) have become fundamental utilities to support China’s socio-economic development. The energy demand of IDC industry, in particular, is sustainably increasing, attracting more and more attention. In this case, energy efficiency and elastic scheduling of flexible workloads is not only prerequisites for the sustainable development of IDC industry, but also the way to assist in achieving the national commitment of “3060” carbon neutrality.

随着数字经济的持续发展和“新基建”国家战略的稳步推进,数据中心成为支撑我国社会经济发展的重要新型基础设施。作为典型高载能行业,数据中心行业的蓬勃发展带来了巨大的耗电量。因此,提升数据中心运行节能水平和灵活运行程度,降低数据中心的用电成本和碳排放,不仅是数据中心自身发展的需要,也是推动“双碳”目标实现的必由之路,对企业的可持续发展具有重大意义。

 

Technically, the execution of some workloads can provide scheduling flexibility potential. In specific, delay-intensive workloads can be postponed in time scale considering their lower priority. Delay-sensitive services can be spatially shifted to another server cluster for processing. Considering the electricity production and carbon emission changing with time and space, the potential of shifting workloads on temporal and spatial dimensions can be utilized for cost reduction, energy saving and carbon-free operation for IDC industry. For achieving this goal, it is essential to explore the capability of IDCs to change the power demand by elastic scheduling of workload and the sensitivity to follow the price and emission signal released from power systems. The coordinated operation of power systems and IDCs can contribute to real-time balance in energy supply and demand and reduce carbon emission. In particular, the current released strategy “East Data Computing in West”, representing moving east data to west areas, further emphasized the necessity of this work.

一般而言,数据中心离线业务和在线业务都具有调度灵活性,即延迟不敏感、优先级较低的离线业务可以在时间维度上调节处理时间,延迟敏感、优先级较高的在线业务可以在空间维度上改变处理的服务器节点。考虑电力供给成本随时间、空间变化的特点,以及不同区域电网的边际碳排放不同的背景,研究业务负载调度带来的数据中心用电特性变化和电力系统运行成本时空分布特性的匹配能力,从而允许数据中心参与电力系统的联合优化运行,对促进区域发展、能源资源供给平衡、缓解能源资源分布不均和减少碳排放的促进作用。特别考虑到国家近期提出的“东数西算”战略,更进一步强化了基于业务负载调度的算力-能源协同运行优化的必要性。

 

In Alibaba Group, Alimama team, which is in overall charge of the advertising business, has successful practical experience in shifting advertising streaming workloads spatially. Moreover, the advertising jobs include both delay-intensive and delay-sensitive workloads. Therefore, in this research, we intend to cooperate with Alimama team in exploiting the feasibility of spatial and temporal scheduling of advertising jobs theoretically and practically, such as scheduling algorithm design, power consumption model establishment, and practical implementation. This research project is the first step of “East Data Computing in West” implementation. And, it also makes Alibaba the first promoter of carbon reduction by workload scheduling in China. The pilot experience of this project will be replicable in China.

考虑到集团阿里妈妈事业群的广告算法部门具有成功将部分广告业务在空间调度的实践经验,而且业务范围涉及在线业务和离线业务。因此,本部门拟与阿里妈妈广告算法团队通力合作,分别从能耗模型和调度算法、理论设计和实践落地两个方面互补互助,以阿里妈妈广告业务负载的时空调度为主要研究对象,以点带面地探索数据中心时空维度调度的可行性。本项目迈出了我国“东数西算”项目实践的第一步,同时使阿里云数据中心成为促进我国互联网行业碳减排的示范者和推动者,项目成果将在全国积累可复制可推广的试点经验。

 

In addition, this project will cooperate with Jibei power grid, Jiangsu power grid, Zhejiang power grid, and western Inner Mongolia power grid in fully awareness of the cost and resource difference. This project can increase the renewable integration and quantify the ability to reduce IDC industries in different regions.

此外,本项目将与冀北电网、江苏电网、浙江电网、蒙西电网合作沟通,充分考虑各地方电网间的成本差异、资源差异进行算力调度,量化其促进当地电网消纳可再生能源、降低数据中心行业碳排放的能力;同时,依靠“全国一体化算力网络”的利好政策,推动政策试点、工程试点在内蒙古、长三角和京津冀地区枢纽节点的建设实施。

Target

This project aims to propose a quantitative model for IDC workload scheduling that considers the actual operation requirements of workloads and scheduling systems. Then, the proposed model is applied in a power market scenario to measure the carbon reduction that IDCs can achieve by workload scheduling. Finally, the proposed model will be used in some pilots to participate in electric markets, electricity ancillary services markets, or demand response markets to evaluate the effectiveness of the proposed model in cost saving and carbon reduction. And the performance of IDC participating in the cooperation with power systems is also quantified.

本项目旨在提出一种考虑工作负载和调度系统实际运行需求的数据中心工作负载调度量化模型。然后,所提的模型会在电力市场的场景中进行应用,来衡量数据中心能够通过负载调度来减少的碳排放量。最后,模型会在一些试点地区参与电力市场、电力辅助服务市场或需求响应市场,来评估模型在减少运营成本和碳排放的实际有效性,同时量化数据中心在参与电力系统协同优化方面的作用。

This project proposes to conduct an in-depth research on three technical problems:

本项目拟从三个技术问题开展深入研究。

  • A mathematical model of our current scheduling system, including resource allocation and virtual machine allocation of different kinds of workloads.

一种描述我们当前调度系统的数学模型,包括不同工作负载的资源分配和虚拟机分配模块。

  • A simulation model of the cooperation of IDCs and the power system, considering workload scheduling and power system operation

一种考虑算力调度和电力系统协同运行的模拟模型

  • A scalable testing environment for testing the reliability and feasibility of the cooperation model

建立一种能扩展的测试环境,以测试项目提出的协同运行模型的有效性


Related Research Topics

  • Analysis on the scheduling boundaries based on the current requirements of scheduling systems for different kinds of workloads.

根据业务调度系统要求,对各类实际调度约束条件进行分析评估。

  • A data-driven model of the mapping relationship between workloads execution and power load of servers or IDCs.

基于数据驱动方法的业务负载和数据中心用电外特性映射关系建模。

  • A cooperation model between IDCs and power systems considering workload elastic scheduling, considering actual scheduling boundaries.

建立考虑灵活性调度约束条件的数据中心和电力系统协同运行模型。

  • Detailed implementation plans of IDC participation in the power markets, electricity ancillary service markets, or demand response markets for our different IDC bases.

针对不同数据中心基地,设计数据中心参与电力市场、电力辅助市场、或者需求响应市场的详细实施方案

  • Evaluate the performance of the proposed model in carbon and cost reduction for different IDC bases.

针对试点实际情况,对负载调度所产生的碳减排量进行评估和量化,将研究成果进行实践。

  • Sensitivity analysis of electricity price, energy resources, and carbon intensity on the performance of the proposed model in carbon and cost reduction.

研究电力价格、电源种类、碳排放强度等指标作为业务负荷调度因子对降低云计算用能成本和碳减排的作用。

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