SHF: SMALL: Collaborative Research: Cloud Mentoring: Guiding Cloud Users for Cost Performance through Testing and Recommendation

SHF：小型：协作研究：云指导：通过测试和推荐指导云用户提高成本绩效

• 批准号: 1618310
• 负责人: Lori Pollock
• 金额: $ 20.64万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618310&HistoricalAwards=false
• 关键词: SHF; SMALL; Collaborative; Research; Cloud

Cloud computing is growing rapidly, with businesses, institutions and individuals moving their workloads to clouds. Cloud users benefit from low cost ownership, a pay-as-you-go pricing model where they only pay for the procured resource usage, and the ability to dynamically scale the resource usage up and down. However, the applications running in clouds usually experience unpredictable performance, which makes it extremely challenging for the users to choose resource configurations that meet their cost and performance requirements. This problem is further complicated as users do not have physical control over cloud computers and are forced to make their decisions based on convoluted cloud performance reports.This research addresses the need to support users in achieving their cost-performance requirements as they port their applications to various cloud services. In particular, the research is embodied in an envisioned testing and recommendation system that determines proper resource management policies that meet performance and cost requirements. By taking a software-testing-based approach, the research provides solutions using only user-accessible information to satisfy user requirements, addresses the limits of static analysis techniques that rely on performance predictability.Such a testing and recommendation system enables non-experts to port their applications to various clouds in a cost effective way. The novel framework, testing and recommendation approaches, data sets, and experimental infrastructure developed within the project will be released open source to advance knowledge and understanding within software engineering and cloud computing. The PIs will continue to involve students of underrepresented groups and continue their involvement in mentoring workshops for students.

云计算正在迅速增长，企业，机构和个人将工作量移至云。云用户受益于低成本所有权，这是一种您只为所需的资源使用付费的付费定价模型，以及在上下动态扩展资源使用量的能力。但是，在云中运行的应用程序通常会经历不可预测的性能，这使用户选择满足其成本和绩效要求的资源配置非常具有挑战性。由于用户没有对云计算机的物理控制，因此该问题进一步复杂，并且被迫根据复杂的云绩效报告做出决策。本研究解决了需要支持用户在将其应用程序移植到各种云服务的情况下实现其成本绩效要求的必要性。特别是，该研究体现在一个设想的测试和建议系统中，该系统决定了满足绩效和成本要求的适当资源管理政策。通过采用基于软件测试的方法，该研究仅使用可访问用户的信息来满足用户要求提供解决方案，以解决依赖性能可预测性的静态分析技术的限制。类似的测试和建议系统使非专家可以以成本效益的方式将其应用程序移植到各种云中。在项目中开发的新型框架，测试和建议方法，数据集以及实验基础架构将被发布开源，以提高软件工程和云计算中的知识和理解。 PI将继续让代表性不足的团体的学生参与，并继续参与指导学生的讲习班。

项目成果

A statistics-based performance testing methodology for cloud applications

• DOI: 10.1145/3338906.3338912
• 发表时间: 2019-08
• 期刊: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Sen He;Glenna Manns;John Saunders;Wei Wang;L. Pollock;M. Soffa

Flexible VM Provisioning for Time-Sensitive Applications with Multiple Execution Options

具有多个执行选项的时间敏感型应用程序的灵活虚拟机配置

• 发表时间: 2018
• 期刊: IEEE International Conference on Cloud Computing
• 作者: Begam, Rehana;Moradi, Hamidreza;Wang, Wei;Zhu, Dakai
• 通讯作者: Zhu, Dakai

Testing Cloud Applications under Cloud-Uncertainty Performance Effects

• DOI: 10.1109/icst.2018.00018
• 发表时间: 2018-04
• 期刊: 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST)
• 作者: Wei Wang;Ningjing Tian;Sunzhou Huang;Sen He;Abhijeet Srivastava;M. Soffa;L. Pollock

iReplayer: In-situ and Identical Record-and-Replay for Multithreaded Applications

iReplayer：多线程应用程序的原位和相同的记录和重放

• 发表时间: 2018
• 期刊: ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI
• 作者: Liu, Hongyu;Silvestro, Sam;Wang, Wei;Tian, Chen;Liu, Tongping
• 通讯作者: Liu, Tongping