To ensure cost-effectiveness of the storage service, the object stores use hard disk drives (HDDs). However, the lower performance of HDDs affect tenants who have strict performance requirements for their big data applications. The use of faster storage devices such as solid state drives (SSDs) is thus desirable by the tenants, but incurs significant maintenance costs to the provider. We design a tiered object store for the cloud, which comprises both fast and slow storage devices. The resulting hybrid store exposes the tiering to tenants with a dynamic pricing model that is based on the tenants’ usage and the provider’s desire to maximize profits. The tenants leverage knowledge of their workloads and current pricing information to select a data placement strategy that would meet the application requirements at the lowest cost.

CAST is a Cloud Analytics Storage Tiering solution that cloud tenants can use to reduce monetary cost and improve performance of analytics workloads. The approach takes the first step towards providing storage tiering support for data analytics in the cloud. CAST performs offline workload profiling to construct job performance prediction models on different cloud storage services, and combines these models with workload specifications and high-level tenant goals to generate a cost-effective data placement and storage provisioning plan. Furthermore, we build CAST++ to enhance CAST's optimization model by incorporating data reuse patterns and across-jobs interdependencies common in realistic analytics workloads.