CRII: CSR: Online Performance Modeling of Opaque Cloud Applications

Many online services are now hosted on the cloud. However, cloud adoption is still very limited when it comes to performance sensitive users. One of the primary challenges for cloud users is the lack of understanding of how cloud resource allocation relates to application performance. While Cloud Service Providers (CSPs) offer users easy access to cloud resources for their computing needs, they do not provide any guarantees on the performance of a user's deployment or any guidelines on how users should set their resource allocations. This is because user deployments are opaque: CSPs cannot control or access a user's workload or application. To make matters worse, the effective capacity of a user's cloud instance can change dynamically due to interference from other users. As a result, cloud deployments are plagued with performance issues. The goal of this research is to develop novel performance models to help users and CSPs understand the dynamic resource requirements of cloud applications without requiring any extensive benchmarking or instrumentation. The research team is constructing novel workload-specific performance models that capture the relationship between cloud resource allocation and application performance. The team is also developing novel online tools based on control theory and machine learning to dynamically infer the (possibly changing) unobservable cloud parameters, thus allowing the performance models to be tuned online. The resulting models will enable users and CSPs to accurately allocate cloud resources to achieve the desired application performance. Cloud-based services are becoming increasingly popular. This project helps improve cloud adoption and promotes more efficient use of the cloud. The research provides tools to reduce wastage of cloud resources, thus helping cloud users reduce their expenditure and also lowering the power consumption in CSP data centers.