Storage workload identification is the task of characterizing a workload in a storage system (more specifically, network storage system—NAS or SAN) and matching it with the previously known workloads. We refer to storage workload identification as “workload identification” in the rest of this article. Workload identification is an important problem for cloud providers to solve because (1) providers can leverage this information to colocate similar workloads to make the system more predictable and (2) providers can identify workloads and subsequently give guidance to the subscribers as to associated best practices (with respect to configuration) for provisioning those workloads. Historically, people have identified workloads by looking at their read/write ratios, random/sequential ratios, block size, and interarrival frequency. Researchers are well aware that workload characteristics change over time and that one cannot just take a point in time view of a workload, as that will incorrectly characterize workload behavior. Increasingly, manual detection of workload signature is becoming harder because (1) it is difficult for a human to detect a pattern and (2) representing a workload signature by a tuple consisting of average values for each of the signature components leads to a large error. In this article, we present workload signature detection and a matching algorithm that is able to correctly identify workload signatures and match them with other similar workload signatures. We have tested our algorithm on nine different workloads generated using publicly available traces and on real customer workloads running in the field to show the robustness of our approach.