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Modern hybrid cloud infrastructures require software to be easily portable between heterogeneous clusters. Application containerization is a proven technology to provide this portability for the functionalities of an application. However, to ensure performance portability, dependable verification of a cluster's performance under realistic workloads is required. Such verification is usually achieved through benchmarking the target environment and its storage in particular, as I/O is often the slowest component in an application. Alas, existing storage benchmarks are not suitable to generate cloud native workloads as they do not generate any storage control operations (e.g., volume or snapshot creation), cannot easily orchestrate a high number of simultaneously running distinct workloads, and are limited in their ability to dynamically change workload characteristics during a run. In this paper, we present the design and prototype for the first-ever Cloud Native Storage Benchmark—CNSBench. CNSBench treats control operations as first-class citizens and allows to easily combine traditional storage benchmark workloads with user-defined control operation workloads. As CNSBench is a cloud native application itself, it natively supports orchestration of different control and I/O workload combinations at scale. We built a prototype of CNSBench for Kubernetes, leveraging several existing containerized storage benchmarks for data and metadata I/O generation. We demonstrate CNSBench's usefulness with case studies of Ceph and OpenEBS, two popular storage providers for Kubernetes, uncovering and analyzing previously unknown performance characteristics. 

Storage benchmarking tools and methodologies today suffer from a glaring gap—they are incomplete because they omit storage control operations, such as volume creation and deletion, snapshotting, and volume reattachment and resizing. Control operations are becoming a critical part of cloud storage systems, especially in containerized environments like Kubernetes, where such operations can be executed by regular non-privileged users. While plenty of tools exist that simulate realistic data and metadata workloads, control operations are largely overlooked by the community and existing storage benchmarks do not generate control operations. Therefore, for cloud native environments, modern storage benchmarks fall short of serving their main purpose—holistic and realistic performance evaluation. Different storage provisioning solutions implement control operations indifferent ways, which means we need a unified storage benchmark to contrast and comprehend their performance and expected behaviors. In this position paper, we motivate the need for a cloud native storage benchmark by demonstrating the effect of control operations on storage provisioning solutions and workloads. We identify the challenges and requirements when implementing such benchmark and present our initial ideas for its design.