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持续优化，以保持性能，即使其部署和工作负载环境发生变化。这适用于内容交付网络等传统系统和边缘/云系统等新兴架构。动态数据驱动系统的设计需要理论进步和新的系统架构。一个关键的挑战是在最优性（即在性能和/或成本方面为当前环境选择最佳部署）与平滑性（即确保部署更改在任何时候都不会太昂贵）之间进行权衡。该项目旨在开发机器学习和优化交叉点的工具，使系统能够在最优性和平滑性之间取得平衡。此外，该项目在 360 度视频流的背景下部署和实证评估这些工具作为代表性案例研究。平滑度不是传统的系统性能衡量标准，因此通常仅由现有系统以临时方式强制执行。然而，对于寻求持续优化其配置的系统来说，这是一个至关重要的考虑因素，因为与更改配置相关的切换成本可能很高。以严格的方式管理最优性和平滑性之间的权衡可以带来显着的改进；然而，它具有挑战性，因为它需要强大的数据驱动设计，可以在不了解未来环境的情况下确定当前是否值得承担转换成本。该项目开发的分析工具能够设计动态系统的算法，通过数据驱动和优化方法的集成来平衡最优性和平滑性。还计划进行 360 度视频流测试台部署活动。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。虽然我们选择视频流作为我们的目标应用，但所提出的基础研究适用范围更广泛。值得注意的是，该项目通过 K-12 和本科生级别的项目扩大了 STEM 领域代表性不足群体的参与。计划的活动包括为中学生开发加速数学课程、为初中生和高中生开设暑期课程以及为本科生开设暑期研究计划。这是与马萨诸塞大学阿默斯特分校、加州理工学院和纽约州立大学石溪分校的研究人员合作的项目。该项目的结果将保存在项目网站上：https://groups.cs.umass.edu/hajiesmaili/soco/。这些将包括研究成果的技术报告、设计的算法的软件原型、数据集以及为 360 个视频流实验收集的实验结果。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进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

Competitive Control with Delayed Imperfect Information

延迟不完全信息的竞争控制

• DOI: 10.23919/acc53348.2022.9867421
• 发表时间: 2022-06
• 期刊: IEEE ACC
• 作者: Yu, Chenkai;Shi, Guanya;Chung, Soon;Yue, Yisong;Wierman, Adam
• 通讯作者: Wierman, Adam

Communication-aware scheduling of precedence-constrained tasks on related machines

相关机器上优先级受限任务的通信感知调度

• DOI: 10.1016/j.orl.2023.11.001
• 发表时间: 2023-11
• 期刊: Operations Research Letters
• 影响因子: 1.1
• 作者: Su, Yu;Vardi, Shai;Ren, Xiaoqi;Wierman, Adam
• 通讯作者: Wierman, Adam

Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems

学习增强度量任务系统的最佳鲁棒性-一致性权衡

• 发表时间: 2023-04
• 期刊: roceedings of The 26th International Conference on Artificial Intelligence and Statistics
• 作者: Nicolas Christianson; Junxuan Shen
• 通讯作者: Junxuan Shen

Minimization Fractional Prophet Inequalities for Sequential Procurement

顺序采购的分数先知不等式最小化

• DOI: 10.1287/moor.2021.173
• 发表时间: 2023-06
• 期刊: Mathematics of Operations Research
• 影响因子: 1.7
• 作者: Qin, Junjie;Vardi, Shai;Wierman, Adam
• 通讯作者: Wierman, Adam

Chasing convex bodies and functions with black-box advice

用黑盒建议追逐凸体和函数

• 发表时间: 2022-01
• 期刊: COLT
• 作者: Nicolas Christianson; Tinashe Handina
• 通讯作者: Tinashe Handina