Estimating resource costs of data-intensive workloads in public clouds

Abstract

The promise of "infinite" resources given by the cloud computing paradigm has led to recent interest in exploiting clouds for large-scale data-intensive computing. In this paper, we present a model to estimate the resource costs for executing data-intensive workloads in a public cloud. The cost model quantifies the cost-effectiveness of a resource configuration for a given workload with consumer performance requirements expressed as SLAs, and is a key component of a larger framework for resource provisioning in clouds. We instantiate the cost model for the Amazon cloud, and experimentally evaluate the impact of key factors on the accuracy of the model.