Key-value solid-state drives (KV-SSDs) are considered as a potential storage solution for large-scale key-value (KV) store applications. Unfortunately, the existing KV-SSD designs are tuned for a specific type of workload, namely, those in which the size of the values are much larger than the size of the keys. Interestingly, there also exists another type of workload, in practice, in which the sizes of keys are relatively large. We re-evaluate the current KV-SSD designs using such unexplored workloads and document their significantly-degraded performance. Observing that the performance problem stems from the increased size of the metadata, we subsequently propose a novel KV-SSD design, called AnyKey, which prevents the size of the metadata from increasing under varying sizes of keys. Our detailed evaluation using a wide range of real-life workloads indicates that AnyKey outperforms the state-of-the-art KV-SSD design under different types of workloads with varying sizes of keys and values.

When an SSD is used for executing multiple work-loads, its internal resources should be allocated to prevent the competing workloads from interfering with each other. While channel-based allocation strategies turn out to be quite effective in offering performance isolation, questions like “what is the optimal allocation?” and “how can one efficiently search for the optimal allocation?” remain unaddressed. To this end, we explore the channel allocation problem in SSDs and employ a reinforcement learning-based approach to address the problem. Specifically, we present an autonomic channel allocating SSD, called AutoAlloc, which can seek near-optimal channel allocation in a self-learning fashion for a given set of co-running workloads. The salient features of AutoAlloc include the following: (i) the optimal allocation can change depending on the user-defined optimization metrics; (ii) the search process takes place in an online setting without any need of extra workload profiling or performance estimation; and, (iii) the search process is fully-automated without requiring any user intervention. We implement AutoAlloc in LightNVM (the Linux subsystem) as part of the FTL, which operates with an emulated Open-Channel SSD. Our extensive experiments using various user-defined optimization metrics and workload execution scenarios indicate that AutoAlloc can find a near-optimal allocation after examining only a very limited number of candidate allocations.

Redfish is a next-generation API standard for the management of data center infrastructures. This rich API can flexibly obtain data using a query string from the client side. However, this feature is optional and not fully supported by many services. We implemented a prototype Redfish query processing module on Nginx, a well-known open source web server. The Redfish query processing module can run with a proxy module and work with any server-side or client-side applications. Additionally, our prototype implementation can be configured to properly utilize queries, which are supported on a backend server, and improve performance. Our implementation was evaluated on an OpenBMC server and a mockup server and showed potential for performance improvement.