Wang receives $100K grant for cloud system management methodology
Cloud computing is growing rapidly as businesses, institutions and individuals are 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.
Dr. Wang's research addresses the need to support users in achieving their cost-performance requirements as they port their applications to various cloud services. In particular, his 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.
Dr. Wang's ultimate goal is to provide an easy-to-use and low-cost testing methodology to help users accurately estimate and minimize the cost of migrating their applications to the cloud.
This project is supported by a sub-award from a funded NSF project in collaboration with Dr. Mary Lou Soffa from University of Virginia and Dr. Lori Pollock from University of Delaware. (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617390&HistoricalAwards=false)