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The cost and power impact of suboptimal storage configurations is significant in DataCenters (DCs) as inefficiencies are aggregated over several thousand servers and represent considerable losses in capital and operating costs. Designing performance, power and cost-optimized systems requires a deep understanding of target workloads, and mechanisms to effectively model different storage design choices. Traditional benchmarking is invalid in cloud data-stores, representative storage profiles are hard to obtain, while replaying the entire application in all storage configurations is impractical both from a cost and time perspective. Despite these issues, current workload generators are not able to accurately reproduce key aspects of real application patterns.
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