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

大型计算机和网络系统(如数据中心)是推动我们信息社会的主力。然而，由于其庞大的规模、快速变化的工作量以及在资源需求和可用性方面的重大不确定性，它们也越来越难以控制和操作。传统的控制和优化方法依赖于正在研究的系统的精心构建的模型，但在这样一个快速变化的环境中，当系统模型的关键组件未知或不断变化时，这些方法就会变得不够。相反，该项目旨在开发新的方法，可以从最新的实时数据快速学习更新的模型，并将这种实时学习与实时控制相结合，以提高大规模和动态联网计算机系统的效率、适应性和服务质量(Qos)。具体地说，该项目专注于为大数据分析和深度学习培训工作负载提供服务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。智能优势包括：(I)实时学习和控制策略，可以从实时反馈中学习计算和网络作业的服务器依赖特征，以极大地提高运行大型和异质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)针对深度学习培训工作量的独特特征而定制的实时学习和控制策略，它可以快速估计总培训时间和跨不同处理单元的依赖程度，以优化吞吐量和延迟。结果可以使数据中心更快地运行作业并更快地完成它们，从而使计算行业受益，这既可以提高数据中心运行各种快速变化的工作负载的整体效率，也可以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。研究结果可为在线学习和随机控制的一般理论提供参考，对无线网络和在线服务平台等同时具有不确定系统动力学和不确定智能体特征的计算机和网络系统也具有一定的参考价值。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联网)的培训，这对未来的大数据经济至关重要。此外，与研究计算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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.