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上的进步和新的系统架构。一个关键的挑战是最优性之间的权衡，即，在性能和/或成本以及平滑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花费太多。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，该项目部署和经验评估这些工具的背景下，360视频流作为一个代表性的案例研究。平滑度不是传统的系统性能度量，因此它通常仅由现有系统以特定方式强制执行。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，因为与改变配置相关联的切换成本可能很大。以严格的方式管理最优性和平滑性之间的权衡可以带来巨大的改进;然而，这是具有挑战性的，因为它需要一个强大的数据驱动设计，可以确定是否值得在当前产生切换成本，而不知道未来的环境。该项目开发分析工具，使动态系统的算法设计，通过数据驱动和优化方法的集成平衡最优性和平滑性。此外，还计划为360视频流进行测试台部署活动。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。值得注意的是，该项目通过K-12和本科水平的课程扩大了STEM领域代表性不足的群体的参与。计划的活动包括为中学生开发加速数学课程，为初中和高中学生开发暑期课程，为本科生开发暑期研究课程。这是一个与来自马萨诸塞州阿默斯特大学、加州理工学院和斯托尼布鲁克的纽约州立大学的研究人员合作的项目。该项目的结果将在项目网站https://groups.cs.umass.edu/hajiesmaili/soco/上公布。其中包括研究成果的技术报告、设计算法的软件原型、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的法定使命，并通过使用基金会的知识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

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

Scalable Reinforcement Learning for Multiagent Networked Systems

• DOI: 10.1287/opre.2021.2226
• 发表时间: 2019-12
• 期刊: Oper. Res.
• 作者: Guannan Qu;A. Wierman;N. Li