A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

Software Defined SSD : An Architecture to Generalize Data Processing in SSDs

With the advances in non-volatile memory technologies, the access latencies and bandwidth of storage class memories such as PCM, spin-transfer torque RAMs, have improved three orders of magnitude compared to hard disks. Along with these improvements, internal bandwidth of the solid state drives (SSDs) has increased and has exceeded the bandwidth on the Host Interface. Thus the Host Interface is the performance bottleneck when the SSDs are treated as simple block IO devices. With the decrease in access latency of the new memory technologies, the memory latency is a small fraction of the total access latency from the host. These observations indicate that treating SSDs made of fast memories as just block IO devices will restrict the system performance gains. In this work, we propose an Software-Defined SSD (SDSSD) Architecture that expands the scope of an SSD to a smart device with compute logic. By moving computations inside the SSD, the amount of data transferred to the host is reduced, thereby reducing the impact of the host interface bottleneck. The compute logic is made software programmable enabling different host applications to make use of it. The compute logic and the host applications interact using a generic RPC Interface, thus making programming simple. The proposed architecture is designed and implemented on a FPGA prototyping board. The results from the IO benchmarks run on the SDSSD prototype indicate that, bandwidth and latency of SDSSD Architecture matches closely to the performance of the SSD Architecture with IO specific logic for large access size

nKV: near-data processing with KV-stores on native computational storage

Massive data transfers in modern key/value stores resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, have yet to see widespread use. In this paper we introduce nKV, which is a key/value store utilizing native computational storage and near-data processing. On the one hand, nKV can directly control the data and computation placement on the underlying storage hardware. On the other hand, nKV propagates the data formats and layouts to the storage device where, software and hardware parsers and accessors are implemented. Both allow NDP operations to execute in host-intervention-free manner, directly on physical addresses and thus better utilize the underlying hardware. Our performance evaluation is based on executing traditional KV operations (GET, SCAN) and on complex graph-processing algorithms (Betweenness Centrality) in-situ, with 1.4×-2.7× better performance on real hardware – the COSMOS+ platform