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（如数据中心）是推动我们的信息社会的主力。然而，由于其庞大的规模、快速变化的工作量以及资源需求和可用性的重大不确定性，它们也越来越难以控制和运作。传统的控制和优化方法依赖于精心构建的系统模型，但当系统模型的关键组件未知或不断变化时，它们在快速变化的环境中变得不足。相反，该项目旨在开发新的方法，可以快速学习更新的模型，从新鲜的实时数据，并将这种实时学习与实时控制，以提高效率，适应性和服务质量（QoS）的大规模和动态网络计算机系统。具体而言，该项目专注于服务于大数据分析和深度学习培训工作负载的大型数据中心的运营，并开发新的实时学习和随机控制策略，这些策略不仅高效，而且可扩展，能够解释和自适应。 智力上的优点包括：（i）实时学习和控制策略，其可以从实时反馈学习计算和网络作业的服务器相关特征，以大大提高运行大的和异构的工作负载的数据中心的吞吐量，减少作业完成时间，并满足服务期限;以及（ii）针对深度学习训练工作量的独特特征定制的实时学习和控制策略，该方法能够快速估计训练总时间和异构处理单元间的依赖关系，从而优化吞吐量和延迟，对知识发现和教育具有潜在的影响。结果可以使数据中心更快地运行作业并更快地完成作业，从而通过提高运行多样化和快速变化的工作负载的数据中心的整体效率以及提高依赖数据中心进行业务决策和数据分析的用户的满意度来使计算行业受益。这些研究成果将有助于在线学习和随机控制的一般理论，这也将有助于其他计算机和网络系统的不确定系统动力学和不确定代理的功能，如无线网络和在线服务平台。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，这对未来的大数据经济至关重要。此外，与计算研究相结合的外展活动将扩大高中生对在线学习和大数据关键原则的知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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