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

University Of Chicago, Chicago IL

Investigators

Abstract

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.