CNS Core: Small: Integrating Real-Time Learning and Control for Large and Dynamic Networked Computer Systems

CNS 核心：小型：集成大型动态网络计算机系统的实时学习和控制

• 批准号: 2113893
• 负责人: Charlie Hu
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113893&HistoricalAwards=false
• 关键词: CNS; Core; Small; Integrating; Real

Large computer and network systems (such as data centers) are the workhorses driving our information society. However, they are also increasingly difficult to control and operate due to their enormous size, fast-changing workload, and significant uncertainty in resource requirement and availability. Traditional approaches to control and optimization rely on carefully-constructed models of the systems under study, but they become insufficient in such a fast-changing environment when crucial components of the system model are either unknown or constantly changing. Instead, this project aims to develop new methods that can quickly learn an updated model from fresh real-time data, and that integrate such real-time learning with real-time control to improve the efficiency, adaptability, and quality-of-service (QoS) of large-scale and dynamic networked computer systems. Specifically, the project focuses on the operation of large data centers serving big-data analytics and deep-learning training workloads, and develops new real-time learning and stochastic control policies that are not only efficient, but also scalable, able to interpret, and adaptive. The intellectual merits include: (i) real-time learning and control policies that can learn, from real-time feedback, server-dependent features of the computing and network jobs, to greatly improve the throughput of data centers running large and heterogeneous workload, reduce job completing times, and meet service deadlines; and (ii) real-time learning and control policies tailored to the unique features of deep-learning training workload, which can quickly estimate the total training time and the dependency across heterogeneous processing units, to optimize both throughput and delay.The proposed research has the potential to have a lasting impact to knowledge discovery and education. The results could enable data centers to run jobs faster and complete them sooner, and therefore benefit the computing industry, both by improving the overall efficiency of data centers running diverse and fast-changing workload, and by improving the satisfaction of users who rely on data centers for business decisions and data analytics. The research findings may contribute to the general theory of both online learning and stochastic control, which will also be useful for other computer and network systems with both uncertain system dynamics and uncertain agent features, such as wireless networks and online service platforms. Students on the project will be trained on both theoretic tools (including online learning, stochastic control, and data analytics) and system building skills (including cluster computing and data-center networking), which are essential for the future big-data economy. Further, the outreach activity integrated with the research computed will broaden the knowledge of high school students on the key principles of online learning and big-data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大型计算机和网络系统（例如数据中心）是推动我们信息社会的主力。但是，由于其巨大的规模，快速变化的工作量以及资源需求和可用性的明显不确定性，它们也越来越难以控制和运行。传统的控制和优化方法取决于正在研究的系统的精心构造的模型，但是当系统模型的关键组件要么未知或不断变化时，它们在这种快速变化的环境中变得不足。取而代之的是，该项目旨在开发可以从新鲜的实时数据中快速学习更新模型的新方法，并将这种实时学习与实时控制相结合，以提高大型和动态网络计算机系统的效率，适应性和服务质量（QOS）。具体而言，该项目着重于为大数据分析和深入学习培训工作量提供服务的大型数据中心的运行，并制定了新的实时学习和随机控制政策，这些政策不仅有效，而且可以扩展，能够解释和自适应。 智力优点包括：（i）实时学习和控制政策，从实时反馈，计算和网络作业的服务器依赖性功能，大大改善了运行大型和异构工作量的数据中心的吞吐量，减少工作完成时间，并满足服务截止日期； （ii）量身定制的实时学习和控制政策，该政策是根据深度学习培训工作量的独特功能量身定制的，这可以快速估计总训练时间和跨异构处理单元的依赖性，以优化吞吐量和延迟。拟议的研究有可能对知识发现和教育产生持久的影响。结果可以使数据中心能够更快地运行工作并更快地完成工作，因此可以通过提高运行多样化和快速变化的工作量的数据中心的总体效率，以及提高依靠数据中心来依靠数据中心来实现业务决策和数据分析的用户的满意度，从而使计算行业受益。研究发现可能有助于在线学习和随机控制的一般理论，这也将对具有不确定系统动态和不确定代理功能（例如无线网络和在线服务平台）的其他计算机和网络系统有用。该项目的学生将接受理论工具（包括在线学习，随机控制和数据分析）和系统构建技能（包括集群计算和数据中心网络）的培训，这对于未来的大数据经济至关重要。此外，与计算的研究集成的外展活动将扩大高中生在线学习和大数据的关键原则的知识。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的评估。

项目成果

A Case for Task Sampling based Learning for Cluster Job Scheduling

基于任务采样的集群作业调度学习案例

• DOI: 10.1109/tcc.2022.3222649
• 发表时间: 2022
• 期刊: IEEE Transactions on Cloud Computing
• 影响因子: 6.5
• 作者: Jajoo, Akshay;Hu, Y. Charlie;Lin, Xiaojun;Deng, Nan
• 通讯作者: Deng, Nan