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Block traces are widely used for system studies, model verifications, anddesign analyses in both industry and academia. While such traces includedetailed block access patterns, existing trace-driven research unfortunatelyoften fails to find true-north due to a lack of runtime contexts such as useridle periods and system delays, which are fundamentally linked to thecharacteristics of target storage hardware. In this work, we proposeTraceTracker, a novel hardware/software co-evaluation method that allows usersto reuse a broad range of the existing block traces by keeping most theirexecution contexts and user scenarios while adjusting them with new systeminformation. Specifically, our TraceTracker's software evaluation model caninfer CPU burst times and user idle periods from old storage traces, whereasits hardware evaluation method remasters the storage traces by interoperatingthe inferred time information, and updates all inter-arrival times by makingthem aware of the target storage system. We apply the proposed co-evaluationmodel to 577 traces, which were collected by servers from differentinstitutions and locations a decade ago, and revive the traces on ahigh-performance flash-based storage array. The evaluation results reveal thatthe accuracy of the execution contexts reconstructed by TraceTracker is onaverage 99% and 96% with regard to the frequency of idle operations and thetotal idle periods, respectively.

TraceTracker: Hardware/Software Co-Evaluation for Large-Scale I/O Workload Reconstruction