Collaborative Research: CNS Core: Medium: Learning to Cache and Caching to Learn in High Performance Caching Systems

合作研究：CNS 核心：中：学习缓存以及在高性能缓存系统中学习缓存

• 批准号: 1955370
• 负责人: Mohammad Alizadeh
• 金额: $ 17.5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955370&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Caching is fundamental to cloud computing and content distribution, and is important to the vast number of applications and services they support. Crucial performance metrics of a caching algorithm are its ability to quickly and accurately learn a changing popularity distribution. However, there is a serious disconnect between empirical studies using real-world traces that account for popularity changes, and analytical performance analysis results that assume a fixed popularity. A basic goal of this project is to develop a methodology based on online learning and reinforcement learning for caching algorithm design with provable performance guarantees. This enables the systematic design of caching algorithms that can be tailored to a variety of application contexts. The use-case of these algorithms is in high performance caching networks that support large-scale cloud applications and services. Emulation of high-performance caching systems to leverage and to empirically evaluate the online learning algorithms developed supports this goal, and provides a real-world context for the methodology developed. The results will also enhance the performance of content distribution platforms. At the same time the project develops fundamental theories that pertain to the area of machine learning, specifically to online learning. This project aims at optimally utilizing locally available memory and computing resources of caches, while ensuring provably good performance via fast and accurate learning of content popularity. This requires the conjunction of several mathematical tools to analyze online learning algorithms, as well as strong systems development skills to make the algorithms a reality. The project addresses these key challenges in two main themes. The first theme focuses on systematic design of distributed online learning in networks of caches using collaborative filtering for distributed identification of popular content, and multi-agent reinforcement learning for joint learning and content placement. The second theme focuses on building high performing caching systems using the algorithms developed in the first theme, and quantifying the impacts of the algorithms on real-world applications such as Hipster Shop, an open-source e-commerce website, and Spark data-analytics job pipelines. The immediate impact of this project is in creating high performance caching schemes that apply to cloud computing and content distribution networks. This project also advances the fundamental theory of online learning. The project includes an education plan focusing on machine learning and caching, and outreach in the form of summer camps and seminars for high school students.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.

缓存对于云计算和内容分布至关重要，对于他们支持的大量应用程序和服务至关重要。 缓存算法的关键性能指标是其快速，准确地学习不断变化的流行分布的能力。但是，使用现实世界的痕迹来解释了受欢迎程度的变化，而分析性能分析结果则存在固定流行的结果。 该项目的一个基本目标是开发一种基于在线学习和强化学习的方法，用于缓存算法设计，并具有可证明的性能保证。 这使得可以根据各种应用程序上下文量身定制的缓存算法的系统设计。这些算法的用例是在支持大规模云应用程序和服务的高性能缓存网络中。 效仿高性能缓存系统来利用和经验评估开发的在线学习算法支持了这一目标，并为开发的方法提供了现实世界的环境。 结果还将提高内容分布平台的性能。 同时，该项目开发了与机器学习领域（特别是在线学习）有关的基本理论。该项目旨在最佳地利用当地可用的内存和计算库的计算资源，同时通过快速，准确地学习内容流行度来确保良好的性能。这需要几种数学工具的结合来分析在线学习算法，以及强大的系统开发技能，以使算法成为现实。该项目以两个主要主题解决了这些主要挑战。第一个主题侧重于使用协作过滤在缓存网络中分布式在线学习的系统设计，以分布式识别流行内容，以及用于关节学习和内容放置的多机构增强学习。第二个主题着重于使用第一个主题中开发的算法构建高性能的缓存系统，并量化算法对现实世界应用的影响，例如Hipster Shop，开源电子商务网站，以及Spark Data-Analytics工作管道。该项目的直接影响在于创建适用于云计算和内容分配网络的高性能缓存方案。该项目还推进了在线学习的基本理论。该项目包括一项重点是机器学习和缓存的教育计划，并以夏令营和高中生的研讨会的形式推广。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和更广泛的评估来支持的。影响审查标准。

项目成果

CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation

• 发表时间: 2022-01
• 作者: Abdullah Alomar;Pouya Hamadanian;Arash Nasr-Esfahany;Anish Agarwal;MohammadIman Alizadeh;Devavrat Shah