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

缓存一直是高性能、可扩展计算系统设计者始终如一的工具，但它的部署方式如此之多，以至于很难在云系统中标准化和扩展。该项目通过设计和实现通用缓存即服务(CaaS)，将缓存在云级存储系统中的使用提升到“一等公民”。CAAS定义了四个互补维度的变革性技术。首先，它为存储缓存定义了新的抽象和体系结构，使存储堆栈可以轻松地在分布式计算基础架构中嵌入轻量级CaaS客户端。其次，CaaS制定并在理论上分析了在CaaS服务内运行的分布式缓存算法，使得各个CaaS服务器节点协作以实现全局最优缓存决策。第三，分布式CaaS客户端和服务器共同设计，以在其实现中实现严格的持久性和容错性。最后，所有CaaS改进都是由详细的全系统模拟器生成的洞察力推动的，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例的用例展示了这些改进：云、大数据和深度学习。CaaS项目的发现通过阐述和让这些学生群体参与课堂项目和实验室工作，为本科生、硕士和博士生创造了新的教育内容和研究机会。外联活动的重点是从计算机科学专业的少数群体中招募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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.