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Achieving cost-efficient, data-intensive computing in the cloud

Cloud computing providers have recently begun to offer high-performance virtualized flash storage and virtualized network I/O capabilities, which have the potential to increase application performance. Since users pay for only the resources they use, these new resources have the potential to lower overall cost. Yet achieving low cost requires choosing the right mixture of resources, which is only possible if their performance and scaling behavior is known. In this paper, we present a systematic measurement of recently introduced virtualized storage and network I/O within Amazon Web Services (AWS). Our experience shows that there are scaling limitations in clusters relying on these new features. As a result, provisioning for a large-scale cluster differs substantially from small-scale deployments. We describe the implications of this observation for achieving efficiency in large-scale cloud deployments. To confirm the value of our methodology, we deploy cost-efficient, high-performance sorting of 100 TB as a large-scale evaluation.