About: Solid-state storage is a research topic. Over the lifetime, 439 publications have been published within this topic receiving 8943 citations. The topic is also known as: SSS.
TL;DR: An automated tool is described that, given device models and a block-level trace of a workload, determines the least-cost storage configuration that will support the workload's performance, capacity, and fault-tolerance requirements.
Abstract: Recently, flash-based solid-state drives (SSDs) have become standard options for laptop and desktop storage, but their impact on enterprise server storage has not been studied. Provisioning server storage is challenging. It requires optimizing for the performance, capacity, power and reliability needs of the expected workload, all while minimizing financial costs. In this paper we analyze a number of workload traces from servers in both large and small data centers, to decide whether and how SSDs should be used to support each. We analyze both complete replacement of disks by SSDs, as well as use of SSDs as an intermediate tier between disks and DRAM. We describe an automated tool that, given device models and a block-level trace of a workload, determines the least-cost storage configuration that will support the workload's performance, capacity, and fault-tolerance requirements. We found that replacing disks by SSDs is not a costeffective option for any of our workloads, due to the low capacity per dollar of SSDs. Depending on the workload, the capacity per dollar of SSDs needs to increase by a factor of 3-3000 for an SSD-based solution to break even with a diskbased solution. Thus, without a large increase in SSD capacity per dollar, only the smallest volumes, such as system boot volumes, can be cost-effectively migrated to SSDs. The benefit of using SSDs as an intermediate caching tier is also limited: fewer than 10% of our workloads can reduce provisioning costs by using an SSD tier at today's capacity per dollar, and fewer than 20% can do so at any SSD capacity per dollar. Although SSDs are much more energy-efficient than enterprise disks, the energy savings are outweighed by the hardware costs, and comparable energy savings are achievable with low-power SATA disks.
TL;DR: A non-volatile solid-state storage subsystem, such as a nonvolatile memory device, maintains usage statistics reflective of the wear state, and thus the remaining useful life, of the subsystem's memory array as mentioned in this paper.
Abstract: A non-volatile solid-state storage subsystem, such as a non-volatile memory device, maintains usage statistics reflective of the wear state, and thus the remaining useful life, of the subsystem's memory array. A host system reads the usage statistics information, or data derived therefrom, from the subsystem to evaluate the subsystem's remaining life expectancy. The host system may use this information for various purposes, such as to (a) display or report information regarding the remaining life of the subsystem; (b) adjust the frequency with which data is written to the subsystem; and/or (c) select the type(s) of data written to the subsystem.
TL;DR: The paper presents an exhaustive analysis of the design space of Gordon systems, focusing on the trade-offs between power, energy, and performance that Gordon must make, and describes a novel flash translation layer tailored to data intensive workloads and large flash storage arrays.
Abstract: As our society becomes more information-driven, we have begun to amass data at an astounding and accelerating rate. At the same time, power concerns have made it difficult to bring the necessary processing power to bear on querying, processing, and understanding this data. We describe Gordon, a system architecture for data-centric applications that combines low-power processors, flash memory, and data-centric programming systems to improve performance for data-centric applications while reducing power consumption. The paper presents an exhaustive analysis of the design space of Gordon systems, focusing on the trade-offs between power, energy, and performance that Gordon must make. It analyzes the impact of flash-storage and the Gordon architecture on the performance and power efficiency of data-centric applications. It also describes a novel flash translation layer tailored to data intensive workloads and large flash storage arrays. Our data show that, using technologies available in the near future, Gordon systems can out-perform disk-based clusters by 1.5× and deliver up to 2.5× more performance per Watt.
TL;DR: In this article, an apparatus, system, and method for solid-state storage as cache for high-capacity, non-volatile storage is described. But the system is based on a cache front-end and a cache back-end.
Abstract: An apparatus, system, and method are disclosed for solid-state storage as cache for high-capacity, non-volatile storage. The apparatus, system, and method are provided with a plurality of modules including a cache front-end module and a cache back-end module. The cache front-end module manages data transfers associated with a storage request. The data transfers between a requesting device and solid-state storage function as cache for one or more HCNV storage devices, and the data transfers may include one or more of data, metadata, and metadata indexes. The solid-state storage may include an array of non-volatile, solid-state data storage elements. The cache back-end module manages data transfers between the solid-state storage and the one or more HCNV storage devices.
TL;DR: In this paper, the authors proposed truncated RAID stripes (fewer storage elements per stripe) to save cached write data when a power failure occurs, which allows the system to maintain RAID parity data protection in a power fail cache flush case even though a full stripe of write data may not exist.
Abstract: Embodiments of the invention are directed to systems and methods for reducing an amount of backup power needed to provide power fail safe preservation of a data redundancy scheme such as RAID that is implemented in solid state storage devices where new write data is accumulated and written along with parity data. Because new write data cannot be guaranteed to arrive in integer multiples of stripe size, a full stripe's worth of new write data may not exist when power is lost. Various embodiments use truncated RAID stripes (fewer storage elements per stripe) to save cached write data when a power failure occurs. This approach allows the system to maintain RAID parity data protection in a power fail cache flush case even though a full stripe of write data may not exist, thereby reducing the amount of backup power needed to maintain parity protection in the event of power loss.