
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | April 22, 2016 |
Latest Amendment Date: | April 22, 2016 |
Award Number: | 1562837 |
Award Instrument: | Standard Grant |
Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | June 1, 2016 |
End Date: | May 31, 2021 (Estimated) |
Total Intended Award Amount: | $304,251.00 |
Total Awarded Amount to Date: | $304,251.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 (480)965-5479 |
Sponsor Congressional District: |
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Primary Place of Performance: |
PO BOX 878809 Tempe AZ US 85287-8809 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Special Projects - CNS, CSR-Computer Systems Research |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Non-volatile memory (NVM) is a transformative technology that is dramatically changing how data storage systems of the
future are built. This technology allows an unprecedented combination of performance and persistence into a single
device. This project will develop a suite of storage caching techniques for this transformative technology along four
complementary dimensions.
The first two usage dimensions address the selective use of NVM as host-side read caches for persistently stored data
as well as using their persistence properties explicitly by developing fault-tolerant write caching solutions. The
latter two develop advanced techniques for delivering storage quality of service (QoS) using NVM caches and building caching
algorithms that are aware of data reduction techniques, such as deduplication and compression, for the NVM layer. Together, these contributions have the potential to transform enterprise data center storage stacks by readily adopting the best properties of current and future NVM technology. The expected performance benefits apply to a broad spectrum of computer systems and applications.
Educational activities will include the involvement of undergraduate students and incorporation of the project's research findings into coursework. Planned outreach activities will focus on recruitment of under-represented students from minority groups in computer science for participation in the project. Transition of the new technologies to practice through open source distribution of the Linux operating system and KVM hypervisor code implementing the innovations are successful expectations.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Non-volatile memory (NVM) is a transformative technology that is dramatically changing how data storage systems of the future are built. This technology allows an unprecedented combination of performance and persistence into a single device. This project has developed a suite of storage caching techniques for this transformative technology along four complementary dimensions.
The first two usage dimensions has addressed the selective use of NVM as host-side read caches for persistently stored data as well as using their persistence properties explicitly by developing fault-tolerant write caching solutions. The latter two has developed advanced techniques for delivering storage quality of service (QoS) using NVM caches and building caching algorithms that are aware of data reduction techniques, such as deduplication and compression, for the NVM layer. Together, these contributions help transform enterprise data center storage stacks by readily adopting the best properties of current and future NVM technology. The performance benefits apply to a broad spectrum of computer systems and applications.
Educational activities include the involvement of undergraduate students and incorporation of the project's research findings into coursework. The outreach activities have focused on recruitment of under-represented students from minority groups in computer science for participation in the project. Transition of the new technologies to practice through open-source distribution of the Linux operating system and KVM hypervisor code implementing the innovations are successful expectations.
The project has produced many important results. For example, CloudCache provides on-demand cache management based on a new cache demand model (reuse working set), which can satisfy VMs’ dynamic cache demands while minimizing space wastage and device wear-out; CacheDedup is the first deduplication solution optimized for caches which provides integrated cache and deduplication management and novel duplication-aware cache replacement algorithms; and Cacheus is a new class of fully adaptive, machine-learned caching algorithms that utilize a combination of experts designed carefully to address newly identified workload primitive types such as scan and churn.
The results of this project have generated broader impacts in several key aspects. First, the findings of the project have improved the content of various courses at ASU in the areas of Cloud Computing and Operating Systems, at the undergraduate, masters, and Ph.D. levels. The project has also been a source of undergraduate research experience and encouraged many of the participants to pursue graduate studies. Second, the project has provided critical systems software solutions for efficiently managing host-side caches within the modern datacenter including bare-bones and virtualized servers and storage. These solutions can significantly improve datacenter resource usage efficiency and workload performance guarantees. Third, research artifacts of the project have been broadly disseminated, including many publications, open-source software, and trace data. Finally, the project has developed important research underpinnings for making the infrastructure for cloud and big-data systems better performing and more robust at a lower cost. Fundamentally, the caching contributions benefit the datacenters by significantly reducing the hardware requirements to run workloads. This directly addresses both cost and energy footprints within datacenters. The contributions of the project also enable performance guarantees in workloads and significant efficiencies in cache resource usage within such environments that directly translate into cost-savings for the datacenter operators and better end-user experience.
Last Modified: 09/29/2021
Modified by: Ming Zhao
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