Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds-Reference-Cited by-同舟云学术

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Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds

Published:2014-12 Issue: Volume: Page:
ISSN:
Container-title:2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing
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Author:

Kim In Kee,Steele Jacob,Qi Yanjun,Humphrey Marty

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7027114/7027326/07027512.pdf?arnumber=7027512

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Workload Characterization in HPC Environment for Auto-scaling of Resources – Preliminary Study;2022 IEEE 19th India Council International Conference (INDICON);2022-11-24

2. A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud;Concurrency and Computation: Practice and Experience;2020-07-22

3. Management of container-based genetic algorithm workloads over cloud infrastructure;Proceedings of the 17th ACM International Conference on Computing Frontiers;2020-05-11

4. Forecasting Cloud Application Workloads with CloudInsight for Predictive Resource Management;IEEE Transactions on Cloud Computing;2020

5. A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing;2018 IEEE 11th International Conference on Cloud Computing (CLOUD);2018-07

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