Collaborative Research: CNS Core: Medium: Dynamic Data-driven Systems - Theory and Applications

合作研究：CNS 核心：媒介：动态数据驱动系统 - 理论与应用

• 批准号: 2106403
• 负责人: Adam Wierman
• 金额: $ 36万
• 依托单位: California Institute of Technology
• 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=2106403&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Modern computer systems must be continually optimized in a data-driven manner to maintain performance, even as their deployment and workload environments change. This holds for traditional systems like content delivery networks and emerging architectures such as edge/cloud systems. The design of dynamic data-driven systems requires both theoretical advancements and new systems architectures. A key challenge is a tradeoff between optimality, i.e., choosing an optimal deployment for the current environment in terms of performance and/or cost, and smoothness, i.e., ensuring that the deployment changes are not too costly at any point. This project seeks to develop tools at the intersection of machine learning and optimization that enable systems to balance between optimality and smoothness. Further, this project deploys and empirically evaluates these tools in the context of 360 video streaming as a representative case study. Smoothness is not a traditional system performance measure, and so it is typically enforced only in ad hoc ways by existing systems. However, it is a crucial consideration for systems that seek to continuously optimize their configuration since the switching costs associated with changing configurations can be significant. Managing the tradeoff between optimality and smoothness in a rigorous fashion can lead to dramatic improvements; however, it is challenging since it requires a robust data-driven design that can determine whether it is worth incurring a switching cost in the present, without knowledge of the future environment. This project develops analytic tools that enable the design of algorithms for dynamic systems that balance optimality and smoothness through the integration of data-driven and optimization approaches. There are also planned test-bed deployment activities for 360 video streaming. The project will provide new foundational tools for the design of dynamic systems across multiple application areas. While we choose video streaming as our target application, the proposed fundamental research is applicable much more broadly. Notably, this project broadens the participation of underrepresented groups in STEM areas through programs at both K-12 and undergraduate levels. Planned activities include developing accelerated mathematics programs for middle-school students, summer programs for middle-school and high-school students, and summer research programs for undergraduate students. This is a collaborative project with investigators from the University of Massachusetts Amherst, California Institute of Technology, and the State University of New York at Stony Brook. The results of this project will be maintained on the project website at https://groups.cs.umass.edu/hajiesmaili/soco/. These will include technical reports of the research findings, software prototypes of the algorithms designed, datasets, and experimental results collected for the 360 video streaming experiments.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.

现代计算机系统必须以数据驱动的方式不断优化，以保持性能，即使它们的部署和工作负载环境发生了变化。这适用于传统系统，例如内容交付网络和新兴体系结构，例如Edge/Cloud Systems。动态数据驱动系统的设计需要理论进步和新的系统体系结构。一个关键的挑战是最优性之间的权衡，即，在绩效和/或成本和平稳性方面为当前环境选择最佳部署，即确保部署变化在任何时候都不会太昂贵。该项目旨在在机器学习和优化的交汇处开发工具，从而使系统能够在最优性和光滑度之间取得平衡。此外，该项目在360个视频流作为代表性案例研究的背景下部署并经验评估了这些工具。平滑度不是传统的系统性能度量，因此通常仅通过现有系统以临时方式执行它。但是，对于试图连续优化其配置的系统，这是一个至关重要的考虑因素，因为与更改配置相关的开关成本可能很重要。以严格的方式管理最优性与光滑之间的权衡会导致巨大的改进；但是，这是具有挑战性的，因为它需要强大的数据驱动设计，该设计可以确定是否值得在当前不了解转换成本，而无需了解未来的环境。该项目开发了分析工具，可以为动态系统设计算法设计，通过集成数据驱动和优化方法来平衡最佳和平滑度。还有360个视频流的计划的测试床部署活动。该项目将为跨多个应用领域的动态系统设计提供新的基础工具。当我们选择视频流作为目标应用程序时，提出的基础研究更广泛地适用。值得注意的是，该项目通过K-12和本科级别的计划扩大了代表性不足的群体在STEM地区的参与。计划的活动包括为中学生开发加速的数学课程，中学和高中生的夏季计划以及针对本科生的夏季研究计划。这是一个合作项目，与马萨诸塞大学阿默斯特大学，加利福尼亚理工学院和纽约州立大学斯托尼·布鲁克大学的调查员。该项目的结果将在项目网站https://groups.cs.umass.edu/hajiesmaili/soco/上维护。这些将包括有关研究结果的技术报告，设计的算法的软件原型，数据集以及为360视频流实验收集的实验结果。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审查标准来通过评估来支持的。

项目成果

The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting

在线暂停和恢复问题：最优算法和碳感知负载转移的应用

• DOI: 10.1145/3626776
• 发表时间: 2023
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Lechowicz, Adam;Christianson, Nicolas;Zuo, Jinhang;Bashir, Noman;Hajiesmaili, Mohammad;Wierman, Adam;Shenoy, Prashant
• 通讯作者: Shenoy, Prashant

Decentralized Online Convex Optimization in Networked Systems

• 发表时间: 2022-07
• 作者: Yiheng Lin;Judy Gan;Guannan Qu;Yashodhan Kanoria;A. Wierman

The Online Knapsack Problem with Departures

出发时的在线背包问题

• DOI: 10.1145/3570618
• 发表时间: 2022
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Sun, Bo;Yang, Lin;Hajiesmaili, Mohammad;Wierman, Adam;Lui, John C.;Towsley, Don;Tsang, Danny H.K.
• 通讯作者: Tsang, Danny H.K.

Smoothed Online Optimization with Unreliable Predictions

• DOI: 10.1145/3579442
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Daan Rutten;Nicolas H. Christianson;Debankur Mukherjee;A. Wierman

Black-Box Acceleration of Monotone Convex Program Solvers

单调凸程序求解器的黑盒加速

• DOI: 10.1287/opre.2022.2352
• 发表时间: 2022
• 期刊: Operations Research
• 影响因子: 2.7
• 作者: London, Palma;Vardi, Shai;Eghbali, Reza;Wierman, Adam
• 通讯作者: Wierman, Adam