Project Period: 06/15/2020 - 05/31/2024
The goals of this project are to:
Caching has been a consistent tool of designers of high-performance, scalable computing systems, but it has been deplo yed 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 w ithin a distributed compute infrastructure. Second, CaaS formulates and theoretically analyzes distributed caching alg orithms that operate within the CaaS service such that individual CaaS server nodes cooperate towards achieving global ly optimal caching decisions. Third, the distributed CaaS clients and servers are co-designed to achieve strict durabi lity and fault-tolerance in their implementations. And finally, all of the CaaS advancements are driven by insights ge nerated from a detailed whole-system simulator that models the diverse cache devices, network configurations, and appl ication 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, Mas ters, 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 di ssemination of research findings which help transition of the new technologies to practice.
This work has been supported by the National Science Foundation award CNS-1956229.