Stanford Technology Ventures Program
Suboptimal storage design has significant cost and power impact in large-scale Data Centers (DCs). Performance, power and cost optimized systems require 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 applications in different storage configurations is impractical both in cost and time. Despite these issues, current workload generators are not able to reproduce key aspects of real application patterns.