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: 1955593
• 负责人: Ming Zhao
• 金额: $ 36.69万
• 依托单位: Arizona State 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 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955593&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，但它以多种方式部署，因此很难在云系统中进行标准化和扩展。该项目通过设计和实现通用的缓存即服务（CaaS），将缓存在云存储系统中的使用提升到“一等公民”的地位。CaaS沿着沿着四个互补的维度定义了变革性技术。首先，它为存储缓存定义了一个新的抽象和体系结构，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制定并从理论上分析了在CaaS服务中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佳缓存决策。第三，分布式CaaS客户端和服务器是共同设计的，以实现严格的持久性和容错性。最后，所有CaaS的进步都是由详细的全系统模拟器生成的见解驱动的，该模拟器对各种缓存设备、网络配置和应用程序需求进行了建模。CaaS项目支持在私有云和公共云中运行的各种应用程序。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，通过这些学生团体在课堂项目和实验室工作中的展示和参与。外联活动的重点是从少数群体中招募计算机科学专业人数不足的学生参加该项目。CaaS项目的成果包括开源软件和研究成果的公开传播，这些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

A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning

• DOI: 10.1109/icassp49357.2023.10095744
• 发表时间: 2023-03
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Kaiqi Zhao;Yitao Chen;Ming Zhao

Knowledge Distillation via Module Replacing for Automatic Speech Recognition with Recurrent Neural Network Transducer

• DOI: 10.21437/interspeech.2022-500
• 发表时间: 2022-09
• 作者: Kaiqi Zhao;H. Nguyen;Animesh Jain;Nathan Susanj;A. Mouchtaris;Lokesh;Gupta;Ming Zhao

Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices

使用可穿戴设备探索基于边缘机器学习的压力预测

• 发表时间: 2022
• 期刊: 21st IEEE International Conference on Machine Learning and Applications
• 作者: S. Sim, T. Paranjpe

Learning Cache Replacement with CACHEUS

学习使用 CACHEUS 进行缓存替换

• 发表时间: 2021
• 期刊: 19th USENIX Conference on File and Storage Technologies (FAST 21
• 作者: Rodriguez, Liana V.;Yusuf, Farzana;Lyons, Steven;Paz, Eysler;Rangaswami, Raju;Liu, Jason;Zhao, Ming;Narasimhan, Giri
• 通讯作者: Narasimhan, Giri

WATSON: A Workflow-based Data Storage Optimizer for Analytics

• 发表时间: 2020
• 作者: Jia Zou;Ming Zhao;Juwei Shi;Chen Wang