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

随着企业、机构和个人将他们的工作负载转移到云上，云计算正在快速增长。云用户受益于低成本拥有、现收现付定价模式(他们只需为采购的资源使用付费)，以及动态调整资源使用的能力。然而，运行在云中的应用程序通常会经历不可预测的性能，这使得用户选择符合其成本和性能要求的资源配置具有极大的挑战性。这个问题变得更加复杂，因为用户对云计算机没有物理控制，被迫根据错综复杂的云性能报告做出决定。本研究旨在解决支持用户在将应用程序移植到各种云服务时实现其性价比要求的需求。特别是，研究体现在一个预想的测试和建议系统中，该系统确定满足性能和成本要求的适当资源管理策略。通过采用基于软件测试的方法，研究提供了仅使用用户可访问的信息来满足用户需求的解决方案，解决了依赖性能可预测性的静态分析技术的局限性，使非专家能够以经济高效的方式将他们的应用移植到各种云中。在该项目中开发的新框架、测试和推荐方法、数据集和实验基础设施将开源发布，以促进软件工程和云计算领域的知识和理解。PIS将继续让代表人数不足的群体的学生参与，并继续参与为学生举办的辅导讲习班。

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.