| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 8, 2013 |
| Latest Amendment Date: | August 8, 2013 |
| Award Number: | 1318572 |
| 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: | October 1, 2013 |
| End Date: | September 30, 2017 (Estimated) |
| Total Intended Award Amount: | $149,997.00 |
| Total Awarded Amount to Date: | $149,997.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
W5510 FRANKS MELVILLE MEMORIAL LIBRARY STONY BROOK NY US 11794-0001 (631)632-9949 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Stony Brook NY US 11794-4400 |
| 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): | 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
The project examines novel services built on top of public cloud
infrastructure to enable cost-effective high-performance computing.
The PIs will explore the on-demand, elastic, and configurable features of
cloud computing to complement the traditional supercomputer/cluster
platforms. More specifically, this research aims to assess the efficacy
of building dynamic cloud-based clusters leveraging the configurability
and tiered pricing model of cloud instances. The scientific value of this
proposal lies in the novel use of untapped features of cloud computing
for HPC and the strategic adoption of small, cloud-based clusters for
the purpose of developing/tuning applications for large supercomputers.
Through this research, the PIs expect to answer key research questions
regarding: (1) automatic workload-specific cloud cluster configuration,
(2) cost-aware and contention-aware data and task co-scheduling,
and (3) adaptive, integrated cloud instance provisioning and job
scheduling, plus workload aggregation for cloud instance rental cost
reduction. If successful, this research will result in tools that
adaptively aggregate, configure, and re-configure cloud resources for
different HPC needs, with the purpose of offering low-cost R&D
environments for scalable parallel applications.
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.
This project examined novel services built on top of public cloud infrastructure to enable cost-effective high-performancecomputing.
It explored on-demand, elastic, and configurable features of cloud computing to complement the traditionalsupercomputer/cluster platforms. More specifically, this research assessed the efficacy of building dynamic cloud-based clusters leveraging the configurability and tiered pricing model of cloud instances. The scientific value of this work lies in the novel use of untapped features of cloud computing for HPC and the strategic adoption of small, cloud-based clustersfor the purpose of developing/tuning applications for large supercomputers.
This research answered key research questions regarding: (1) automatic workload-specific cloud clusterconfiguration, (2) cost-aware and contention-aware data and task co-scheduling, and (3) adaptive, integrated cloud instanceprovisioning and job scheduling, plus workload aggregation for cloud instance rental cost reduction.
The research resulted in tools and platforms that adaptively aggregate, configure, and re-configure cloud resources for different HPC needs, with the purpose of offering low-cost R&D environments for scalable parallel applications. Examples include: LiPS -- a new cost-efficient data and task co-scheduler for MapReduce in cloud environments, AffordHadoop -- a scheduler that can save up to 48% of the overall dollar costs and provide significant flexibility in fine-tuning the cost-performance trade-off, Speedster -- a dynamic Hadoop task scheduler that eliminates stragglers and hence minimizes total job completion time, and FlexStripe -- a new flexible striping method for parallel filesystems that improves overall throughput by 40%.
We trained multiple students (including several female students) to become experts in this new field which combines traditional scheduling and cloud computing research.
Last Modified: 12/03/2017
Modified by: Radu Sion
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