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Center for Research in Systems and Storage (CRSS)

Center Overview

The Center for Research in Systems and Storage (CRSS) conducts research in next-generation data storage systems, paving the way for companies to build and maintain systems that provide secure, reliable, energy-efficient, and cost-effective data storage. Storage longevity, manageability, scalability, and reliability, remain a centerpiece of our research. But, we are also researching distributed computing, memory disaggregation, tiered memory and storage, computational memory, networking, debugging, and security. We are actively researching how to most efficiently utilize tiered memory systems that offer different performance, predictability and cost properties. We are building systems to automate workload characterization (often leveraging ML) and placement within distributed systems, to optimize for performance, cost, and reliability. To achieve these goals, we are exploring technologies including CXL, memory virtualization, and hardware-software co-design.

CRSS is a partnership between academia and industry to explore and develop new technologies and methods. CRSS also facilitates collaboration in research and education, providing pathways to simplify direct transfer of university-developed ideas, research results, and technology to its industrial sponsors. This enables the industrial members to be more competitive in the global marketplace. CRSS also focuses on providing talented graduate and undergraduate students the technical background and industry interaction required to become the next generation of scientists and engineers.

Universities

University of California, Santa Cruz

View Center Website

Center Personnel

Heiner Litz
CRSS Director

hlitz@ucsc.edu

Andrew Quinn
CRSS Associate Director

aquinn1@ucsc.edu

Darrell D. E. Long
CRSS Director Emeritus

darrell@ucsc.edu

Ethan Miller
CRSS Director Emeritus

elm@ucsc.edu

Cynthia McCarley
CRSS Administrative Staff
+1 831 459 2817
cynmccar@ucsc.edu

Research Focus

Application of machine learning to improve storage system performance

Explore machine-learning techniques for system trace generation and analysis.
Investigate techniques to use machine learning to optimize system design in a running system.

Data storage security

Develop techniques to secure data against snooping by hiding it in less-secure data.
Develop ways to quickly and securely delete data to satisfy requirements from initiatives such as Europe's General Data Protection Regulation and California's data privacy laws. These techniques may lead to better approaches for data storage that are secure against snooping, even by untrusted cloud storage providers.

Efficient utilization of byte-addressable nonvolatile memory (NVM), such as Intel Optane, into the storage stack

Explore multiple techniques for supporting NVM persistence across crashes.
Investigate changes to the operating system to better integrate NVM with lower overhead and higher security.

Long-term data storage

Develop techniques to make long-term data storage more reliable and secure.
Investigate the impacts of different storage technologies - flash, shingled disk, DNA storage, glass, and others - on the long-term viability and cost of archival data storage.
Investigate techniques to better leverage long-term data storage by making it easier to run analyses on "cold" data.

Scalable storage systems

Develop approaches that leverage high-bandwidth data center interconnects to provide scalable, reliable flash storage at ultrahigh speeds for use in cloud data center computing.

Awards

1841545

Phase II IUCRC CRSS: Center for Research in Storage Systems

The Center for Research in Storage Systems (CRSS) conducts research to improve the performance, security, reliability, efficiency, and usability of data storage. A system composed of thousands or even millions of storage devices must provide these characteristics, though individual devices may lack them. Finding and using data in such large, complex systems is challenging, yet it must be made simple and efficient for end users. Complex systems are also subject both to hardware and software failures as well as directed attacks to steal or destroy data, so storage systems must guard against both security breaches and data loss. CRSS is concerned with adapting systems to better utilize new storage technologies such as byte-addressable non-volatile memory (BNVM), shingled disk, and glass. Operating systems and file systems must adapt to these new technologies to provide higher performance and better usability for BNVM, and improved security, efficiency, and long-term reliability for archival storage. Cross-cutting issues such as improved security and the usage of machine learning to optimize storage systems are relevant for all types of storage systems, yet there remain many open questions in how to address these issues, particularly given the large scale in both time and size that CRSS is exploring. Improving storage systems for large-scale data science is also a significant challenge that CRSS is addressing. Data storage is the foundation of our digital society: the vast majority of our knowledge and information is stored digitally. The research done at CRSS will allow society as a whole to better keep and utilize the flood of information our world is gathering. CRSS research will help cloud providers provide more reliable and usable large-scale storage, and will help reduce the risk of security breaches. CRSS research will also help organizations store and access the massive amounts of data generated by the Internet of Things, further aiding a wide range of societal issues such as climate, transportation, and housing. CRSS will maintain a Web-accessible project repository at https://www.crss.ucsc.edu/repository.html, which will have links to publications, data, and code developed by all CRSS sites. Code will be hosted on either gitlab.soe.ucsc.edu or bitbucket.org. Traces will be hosted locally or donated to the repository operated by the Storage Networking Industry Association's Input/Output Traces, Tools, and Analysis (IOTTA) Technical Work Group. 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. Read More >

Member Organizations

IUCRC affiliated member organizations are displayed as submitted by the Center. Non-federal organizations are not selected, approved, or otherwise endorsed by the U.S. National Science Foundation.

Hewlett-Packard
Intel
Marvell
Meta
Nutanix
SK Hynix
Western Digital

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.