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NGS: Providing and Maximizing Quality of Service in Utility Computing Servers

NGS: Providing and Maximizing Quality of Service in Utility Computing Servers
NGS:提供并最大化公用计算服务器的服务质量
批准号:
0406306
负责人:
Yan Solihin
金额:
$0.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-09-01 至 2007-08-31

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中文摘要
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英文摘要
This work proposes new run-time, operating system, and performance modeling techniques that enable Quality of Service (QoS) through providing performance guarantee and contention minimization for user jobs in future utility computing servers. Utility Computing is an IT business model where users outsource computing to a vendor that manages computing resources and charges its customers based on the actual computing that they use. Utility computing servers will likely simultaneously run many user jobs with varying performance guarantee requirements.Intellectual Merit:The need to provide performance guarantee in high performance servers. The proposed work seeks to address. The proposed work will make revolutionary advances to the current state of the art technology in batch job submission system, OS job scheduling, and performance modeling. More specifically, the proposed approach consists of three new components: (1) Job Admission Strategy that accepts or rejects jobs based on the servers' ability to meet the requested QoS, (2) Job Scheduling Strategy that boosts the system throughput by minimizing the contention among multiple jobs, and (3) Contention Prediction Model that predicts the degree of contention and its impact on job execution times for a given job schedule.Broader Impact:The proposed work takes QoS issues from their traditional area in computer networks to a new area in servers, run-time systems and operating systems.
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Collaborative Research: CSR: Medium: Scaling Secure Serverless Computing on Heterogeneous Datacenters
Collaborative Research: CNS Core: Medium: Understanding and Strengthening Memory Security for Non-Volatile Memory
Collaborative Research: PPoSS: Planning: Scaling Secure Serverless Computing on Hetergeneous Datacenters
SHF: Small: Collaborative Research: Efficient Memory Persistency for GPUs
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