Software-defined storage

Abstract: With the rapid growth of data centers and the unprecedented increase in storage demands, the traditional storage control techniques are considered unsuitable to deal with this large volume of data in an efficient manner. The Software Defined Storage (SDStore) comes as a solution for this issue by abstracting the storage control operations from the storage devices and set it inside a centralized controller in the software layer. Building a real SDStore system without any simulation and emulation is considered an expensive solution and may have a lot of risks. Thus, there is a need to simulate such systems before the real-life implementation and deployment. In this paper we present SDStorage, an experimental framework to provide a novel virtualized test bed environment for SDStore systems. The main idea of SDStorage is based on the Mininet Software Defined Network (SDN) Open Flow simulator and is built over of it. The main components of Mininet, which are the host, the switch and the controller, are customized to serve the needs of SDStore simulation environments.

Abstract: A storage hypervisor having a software defined storage controller (SDSC) provides for a comprehensive set of storage control, virtualization and monitoring functions to decide the placement of data and manage functions such as availability, automated provisioning, data protection and performance acceleration. The SDSC running as a software driver on the server replaces the hardware storage controller function, virtualizes physical disks in a cluster into virtual building blocks and eliminates the need for a physical RAID layer, thus maximizing configuration flexibility for virtual disks. This configuration flexibility consequently enables the storage hypervisor to optimize the combination of storage resources, data protection levels and data services to efficiently achieve the performance, availability and cost objectives of individual applications. This invention enables complex SAN infrastructure to be eliminated without sacrificing performance, and provides more services than prior art SAN with fewer components, lower costs and higher performance.

Abstract: Object stores are becoming pervasive due to their scalability and simplicity. Their broad adoption, however, contrasts with their rigidity for handling heterogeneous workloads and applications with evolving requirements, which prevents the adaptation of the system to such varied needs. In this work, we present Crystal, the first Software-Defined Storage (SDS) architecture whose core objective is to efficiently support multi-tenancy in object stores. Crystal adds a filtering abstraction at the data plane and exposes it to the control plane to enable high-level policies at the tenant, container and object granularities. Crystal translates these policies into a set of distributed controllers that can orchestrate filters at the data plane based on real-time workload information. We demonstrate Crystal through two use cases on top of OpenStack Swift: One that proves its storage automation capabilities, and another that differentiates IO bandwidth in a multi-tenant scenario. We show that Crystal is an extensible platform to deploy new SDS services for object stores with small overhead.

Abstract: 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.