CRII: CSR: Skeletor: Building a Platform for Quantitative Workload Characterization

Emory University, Atlanta GA

Investigators

Abstract

Combined compute, memory, storage and networking systems are too complex to be modeled correctly. Thus, only way to determine performance of an entire system or a sub-system in the context of a whole system is to test it by applying a set of workloads under controlled conditions. The workloads should be representative of the real applications. In characterizing a system, the workloads that expose diverse system behaviors are more valuable than workloads that behave similarly. Thus, workload characterization is important. This proposal focuses on workloads for storage systems to enable storage systems performance optimization. From a systems perspective, if the behavior of the executing workload can be mapped to one or more of the known behaviors, then the system can be adapted to a configuration(s) that is best suited for that behavior. The goal of this project is to recognize the behavior of the workload to find workload archetype. The project will research workload metrics important to measure and define rigorous archetypes parameterized by these metrics to characterize new workloads without complex, slow predictive analytics or onerous domain specification. The investigator plans to produce a workload classification schema in preparation for developing adaptive, workload-aware automated storage tuning and provisioning framework. The project has two main goals, (1) learning what to measure by creating a parameterized taxonomy of the model workloads; and (2) learning how to measure by creating a framework to infer and categorize functionally distinct workloads. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.