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

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

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.