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Solid-state drives (SSDs) based on NAND flash are making deep inroads into data centers as well as the consumer market. In 2016, manufacturers shipped more than 130 million units totaling around 50 Exabytes of storage capacity. As the amount of data stored on solid state drives keeps increasing, it is important to understand the reliability characteristics of these devices. For a long time, our knowledge about flash reliability was derived from controlled experiments in lab environments under synthetic workloads, often using methods for accelerated testing. However, within the last two years, three large-scale field studies have been published that report on the failure behavior of flash devices in production environments subjected to real workloads and operating conditions. The goal of this paper is to provide an overview of what we have learned about flash reliability in production, and where appropriate contrasting it with prior studies performing controlled experiments. [ABSTRACT FROM PUBLISHER]

Over the last two years, nine IEEE Computer Society tech leaders collaborated to identify important industry advances that promise to change the world by 2022. The 23 technologies provide new insights into the emergence of "seamless intelligence." [ABSTRACT FROM PUBLISHER]

The performance modeling and analysis of disk arrays is challenging due to the presence of multiple disks, large array caches, and sophisticated array controllers. Moreover, storage manufacturers may not reveal the internal algorithms implemented in their devices, so real disk arrays are effectively black-boxes. We use standard performance techniques to develop an integrated performance model that incorporates some of the complexities of real disk arrays. We show how measurement data and baseline performance models can be used to extract information about the various features implemented in a disk array. In this process, we identify areas for future research in the performance analysis of real disk arrays. [ABSTRACT FROM AUTHOR]

Examines the idea of using a variable-speed processor (VSP) that can be operated at a high clock speed, and then slowed down to a lower speed before heat accumulation destroys an electronic circuit. Heat generation; Optimality of the bang-bang control policy; Stochastic modal for the VSP; Controlling the VSP with linear temperature gradients.

Presents analytical methods evaluating the effectiveness of dynamic finite versioning (DFV), with focus on understanding the trade-offs between the average storage overhead and query obsolescence. Presentation of asymptotic closed-forms; Estimation of moments of Y; Effect of skew.

The costs of data loss and unavailability can be large, so businesses use many data protection techniques such as remote mirroring, snapshots, and backups to guard against failures. Choosing an appropriate combination of techniques is difficult because there are numerous approaches for protecting data and allocating resources. Storage system architects typically use ad hoc techniques, often resulting in overengineered expensive solutions or underprovisioned inadequate ones. In contrast, this paper presents a principled automated approach for designing dependable storage solutions for multiple applications in shared environments. Our contributions include search heuristics for intelligent exploration of the large design space and modeling techniques for capturing interactions between applications during recovery. Using realistic storage system requirements, we show that our design tool produces designs that cost up to two times less in initial outlays and expected data penalties than the designs produced by an emulated human design process. Additionally, we compare our design tool to a random search heuristic and a genetic algorithm metaheuristic, and show that our approach consistently produces better designs for the cases we have studied. Finally, we study the sensitivity of our design tool to several input parameters. [ABSTRACT FROM AUTHOR]