CSR: Small: Cost-Aware Cloud Profiling, Prediction, and Provisioning as a Service

CSR：小：具有成本意识的云分析、预测和配置即服务

• 批准号: 1816611
• 负责人: Kyle Chard
• 金额: $ 50万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816611&HistoricalAwards=false
• 关键词: CSR; Small; Cost; Aware; Cloud

Cloud computing has the potential to transform computational practice by enabling immediate, on-demand access to large-scale computing resources. But large-scale cloud computing can easily be costly. The Scalable Cost-Aware Cloud Infrastructure Management and Provisioning (SCRIMP) project aims to develop new cloud access methods that will reduce the complexity and cost and improve the efficiency of using cloud resources. The project will innovate in three areas: profiling, prediction, and provisioning. Its new machine learning-based profiling techniques aim to predict application performance, at different levels of accuracy, across a diverse set of cloud resources, based upon derivation of comparable and related instance classes, explorative profiling techniques, and analysis of historical usage. Its ensemble-based market prediction models will allow the many existing cloud market prediction models to be easily compared and then combined so that their collective strengths can be used to predict costs with the aim of minimizing cost, price risk, and likelihood of instance revocation. Finally, its overarching provisioning model will combine application profiles and market prediction models to enable automated, cost-efficient, policy-based cloud provisioning as well as efficient placement and migration of workload within the resulting dynamically provisioned environment.SCRIMP will advance the use of computation across the sciences, particularly within smaller institutions, by simplifying access to on-demand cloud computing and improving the efficiency with which researchers make use of cloud infrastructure. By lowering scientific computing costs and complexity for many users, SCRIMP will enable more efficient use of cloud credits (whether from cloud providers or funding agencies), democratize access to cloud computing by researchers without dedicated computing infrastructure or expertise, and allow researchers and students to conduct increasingly complex analytics, on larger datasets, and at higher resolution. SCRIMP will also be directly relevant in education, allowing educators to provide access to large resource pools at low cost with guaranteed performance.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.

云计算有潜力通过即时、按需访问大规模计算资源来改变计算实践。但大规模云计算很容易成本高昂。可扩展成本感知云基础设施管理和配置（SCRIMP）项目旨在开发新的云访问方法，以降低复杂性和成本，并提高云资源的使用效率。该项目将在三个领域进行创新：分析、预测和配置。其新的基于机器学习的分析技术旨在基于可比和相关实例类的推导、探索性分析技术以及历史使用情况分析，跨不同的云资源集以不同的准确度预测应用程序性能。其基于集成的市场预测模型将允许轻松比较和组合许多现有的云市场预测模型，以便可以利用它们的集体优势来预测成本，从而最大限度地降低成本、价格风险和实例撤销的可能性。最后，其总体配置模型将结合应用程序配置文件和市场预测模型，以实现自动化、经济高效、基于策略的云配置，以及在生成的动态配置环境中有效放置和迁移工作负载。 SCRIMP 将推进计算的使用通过简化对按需云计算的访问并提高研究人员使用云基础设施的效率，跨科学领域，特别是在较小的机构内。通过降低许多用户的科学计算成本和复杂性，SCRIMP 将能够更有效地使用云积分（无论是来自云提供商还是资助机构），使没有专用计算基础设施或专业知识的研究人员对云计算的访问民主化，并允许研究人员和学生对更大的数据集和更高的分辨率进行日益复杂的分析。 SCRIMP 还将与教育直接相关，使教育工作者能够以低成本和有保证的性能提供大型资源池的访问权限。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Assessing the Current State of AWS Spot Market Forecastability

评估 AWS 现货市场可预测性的现状

• DOI: 10.1109/supercompcloud56703.2022.00007
• 发表时间: 2022-11-01
• 期刊: 2022 IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies (SuperCompCloud)
• 作者: Simon Caton;Matt Baughman;Christian Haas;Ryan Chard;Ian T. Foster;K. Chard
• 通讯作者: K. Chard

Deconstructing the 2017 Changes to AWS Spot Market Pricing

解读 2017 年 AWS 现货市场定价变化

• DOI: 10.1145/3322795.3331465
• 发表时间: 2019-01
• 期刊: 10th Workshop on Scientific Cloud Computing
• 作者: Baughman, Matt;Caton, Simon;Haas, Christian;Chard, Ryan;Wolski, Rich;Foster, Ian;Chard, Kyle
• 通讯作者: Chard, Kyle

Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources

通过具有成本意识的云资源配置来增强自动化 FaaS

• DOI: 10.1109/escience51609.2021.00053
• 发表时间: 2021-09
• 期刊: 2021 IEEE 17th International Conference on eScience (eScience
• 作者: Baughman, Matt;Foster, Ian;Chard, Kyle
• 通讯作者: Chard, Kyle

ParaOpt: Automated Application Parameterization and Optimization for the Cloud

ParaOpt：云的自动化应用程序参数化和优化

• DOI: 10.1109/cloudcom.2019.00045
• 发表时间: 2019-12-01
• 期刊: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
• 作者: Chaofeng Wu;Ian T Foster;Ted Summer;Zhuozhao Li;A. Woodard;Ryan Chard;Matt Baughman;Y. Babuji;K. Chard;Jason Pitt
• 通讯作者: Jason Pitt

FLoX: Federated Learning with FaaS at the Edge

FLoX：边缘 FaaS 的联合学习

• DOI: 10.1109/escience55777.2022.00016
• 发表时间: 2022-10-01
• 期刊: 2022 IEEE 18th International Conference on e-Science (e-Science)
• 作者: Nikita Kotsehub;Matt Baughman;Ryan Chard;Nathaniel Hudson;Panos Patros;Omer F. Rana;I. Foster;K. Chard
• 通讯作者: K. Chard