CNS Core: Medium: Collaborative Research: Generalized Caching-As-A-Service

CNS 核心：媒介：协作研究：通用缓存即服务

• 批准号: 1956229
• 负责人: Raju Rangaswami
• 金额: $ 63.31万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956229&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Collaborative; Research

Caching has been a consistent tool of designers of high-performance, scalable computing systems, but it has been deployed in so many ways that it can be difficiult to standardize and scale in cloud systems. This project elevates the use of caching in cloud-scale storage system to a "first-class citizen" by designing and implementing generalized Caching-as-a-Service (CaaS). CaaS defines transformative technology along four complementary dimensions. First, it defines a new abstraction and architecture for storage caches whereby storage stacks can easily embed lightweight CaaS clients within a distributed compute infrastructure. Second, CaaS formulates and theoretically analyzes distributed caching algorithms that operate within the CaaS service such that individual CaaS server nodes cooperate towards achieving globally optimal caching decisions. Third, the distributed CaaS clients and servers are co-designed to achieve strict durability and fault-tolerance in their implementations. And finally, all of the CaaS advancements are driven by insights generated from a detailed whole-system simulator that models the diverse cache devices, network configurations, and application demand.The CaaS project supports a broad spectrum of applications that run in the private and public clouds. The CaaS project showcases these improvements via use cases in three important computing paradigms: Cloud, Big Data, and Deep Learning. The findings from the CaaS project create new educational content and research opportunities for undergraduates, Masters, and PhD students via exposition and involvement of these student groups within classroom projects and laboratory work. The outreach activities focus on the recruitment of under-represented students from minority groups in Computer Science for participation in the project. The outcomes of the CaaS project include open source software and public dissemination of research findings which help transition of the new technologies to practice.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.

缓存一直是高性能、可扩展计算系统设计者的一致工具，但它的部署方式多种多样，因此很难在云系统中实现标准化和扩展。该项目通过设计和实现通用缓存即服务 (CaaS)，将云规模存储系统中缓存的使用提升为“一等公民”。 CaaS 从四个互补的维度定义了变革性技术。首先，它定义了存储缓存的新抽象和架构，存储堆栈可以轻松地将轻量级 CaaS 客户端嵌入分布式计算基础设施中。其次，CaaS 制定并理论上分析了在 CaaS 服务内运行的分布式缓存算法，以便各个 CaaS 服务器节点合作实现全局最优缓存决策。第三，分布式CaaS客户端和服务器经过共同设计，以在其实施中实现严格的耐用性和容错性。最后，所有 CaaS 进步都是由详细的全系统模拟器生成的见解驱动的，该模拟器对不同的缓存设备、网络配置和应用程序需求进行建模。CaaS 项目支持在私人和公共环境中运行的广泛应用程序云。 CaaS 项目通过三个重要计算范式的用例展示了这些改进：云、大数据和深度学习。 CaaS 项目的研究结果通过在课堂项目和实验室工作中展示和参与这些学生群体，为本科生、硕士生和博士生创造了新的教育内容和研究机会。外展活动的重点是招募计算机科学领域少数群体中代表性不足的学生参与该项目。 CaaS 项目的成果包括开源软件和公开传播研究成果，有助于将新技术转化为实践。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和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

FAB Storage for the Hybrid Cloud

适用于混合云的 FAB 存储

• DOI: 10.1109/nas55553.2022.9925329
• 发表时间: 2022
• 期刊: Architecture and Storage (NAS
• 作者: Rangaswami, Raju

Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended Abstract

使用机器学习进行存储系统跟踪表征、压缩和合成 — 扩展摘要

• 发表时间: 2023
• 期刊: International Conference on Principles of Advanced Discrete Simulation (PADS
• 作者: Pratik Poudel

Allocation Policies Matter for Hybrid Memory Systems (Poster and Extended Abstract)

分配策略对于混合内存系统很重要（海报和扩展摘要）

• 发表时间: 2023
• 期刊: IEEE International Conference on High-Performance Parallel and Distributed Computing
• 作者: Maruf, Adnan;Carlson, Daniel;Ghosh, Ashikee;Saha, Manoj;Bhimani, Janki;Rangaswami, Raju
• 通讯作者: Rangaswami, Raju

Finding optimal non-datapath caching strategies via network flow

通过网络流寻找最佳的非数据路径缓存策略

• 发表时间: 2023
• 期刊: Theoretical computer science
• 影响因子: 1.1
• 作者: Steven Lyons, Raju Rangaswami