Solid-state storage

Summary

Solid-state storage (SSS) is a type of non-volatile computer storage that stores and retrieves digital information using only electronic circuits, without any involvement of moving mechanical parts. This differs fundamentally from the traditional electromechanical storage, which records data using rotating or linearly moving media coated with magnetic material. Solid-state storage devices typically store data using electrically-programmable non-volatile flash memory, however some devices use battery-backed volatile random-access memory (RAM). Having no moving mechanical parts, solid-state storage is much faster than traditional electromechanical storage; as a downside, solid-state storage is significantly more expensive and suffers from the write amplification phenomenon. Solid-state storage devices come in various types, form factors, sizes of storage space, and interfacing options to satisfy application requirements for many different types of computer systems and appliances.

Official source

https://en.wikipedia.org/wiki/Solid-state_storage

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Path Processing using Solid State Storage

Manoussos Gavriil Athanassoulis

Recent advances in solid state technology have led to the introduction of Solid State Drives (SSDs). Todays SSDs store data persistently using NAND ﬂash memory. While SSDs are more expensive than hard disks when measured in dollars per gigabyte, they are signiﬁcantly cheaper when measured in dollars per random I/O per second. Additional storage technologies are under development, Phase Change Memory (PCM) being the next one to enter the marketplace. PCM is nonvolatile, it can be byte-addressable, and in future Multi Level Cell (MLC) devices, PCM is expected to be denser than DRAM. PCM has lower read and write latency compared to NAND ﬂash memory, and it can endure orders of magnitude more write cycles before wearing out. Recent research has shown that solid state devices can be particularly beneﬁcial for latency-bound applications involving dependent reads. Latency-bound applications like path processing in the context of graph processing or Resource Description Framework (RDF) data processing are typical examples of these applications. We demonstrate via a custom graph benchmark that even an early prototype Phase Change Memory device can offer signiﬁcant improvements over mature ﬂash devices (1.5x - 2.5x speedup in response times). We take this observation further by building Pythia, a prototype RDF repository tailor-made for Solid State Storage to investigate the predicted beneﬁts for these type of workloads that can be achieved in a properly designed RDF repository. We compare the performance of our repository against the state of the art RDF-3X repository in a limited set of tests and discuss the results. We ﬁnally compare the performance of our repository running on a PCM-based device against a state of the art ﬂash device, showing that there is indeed signiﬁcant gain to be achieved by using PCM for RDF processing.

2012

Recent advances in solid state technology have led to the introduction of Solid State Drives (SSDs). Todays SSDs store data persistently using NAND flash memory. While SSDs are more expensive than hard disks when measured in dollars per gigabyte, they are significantly cheaper when measured in dollars per random I/O per second. Additional storage technologies are under development, Phase Change Memory (PCM) being the next one to enter the marketplace. PCM is nonvolatile, it can be byte-addressable, and in future Multi Level Cell (MLC) devices, PCM is expected to be denser than DRAM. PCM has lower read and write latency compared to NAND flash memory, and it can endure orders of magnitude more write cycles before wearing out. Recent research has shown that solid statedevices can be particularly beneficial for latency-bound applications involving dependent reads. Latency-bound applications like path processing in the context of graph processing or Resource Description Framework (RDF) data processing are typical examples of these applications.We demonstrate via a custom graph benchmark that even an early prototype Phase Change Memory device can offer significant improvements over mature flash devices (1.5x - 2.5x speedup in response times). We take this observation further by building Pythia, a prototype RDF repository tailor-made for Solid State Storage to investigate the predicted benefits for these type of workloads that can be achieved in a properly designed RDF repository. We compare the performance of our repository against the state of the art RDF-3X repository in a limited set of tests and discuss the results. We finally compare the performance of our repository running on a PCM-based device against a state of the art flash device, showing that there is indeed significant gain to be achieved by using PCM jfor RDF processing.

2013

Solid-State Storage and Work Sharing for Efficient Scaleup Data Analytics

Manoussos Gavriil Athanassoulis

Today, managing, storing and analyzing data continuously in order to gain additional insight is becoming commonplace. Data analytics engines have been traditionally optimized for read-only queries assuming that the main data reside on mechanical disks. The need for 24x7 operations in global markets and the rise of online and other quickly-reacting businesses make data freshness an additional design goal. Moreover, the increased requirements in information quality make semantic databases a key (often represented as graphs using the RDF data representation model). Last but not least, the performance requirements combined with the increasing amount of stored and managed data call for high-performance yet space-efficient access methods in order to support the desired concurrency and throughput. Innovative data management algorithms and careful use of the underlying hardware platform help us to address the aforementioned requirements. The volume of generated, stored and queried data is increasing exponentially, and new workloads often are comprised of time-generated data. At the same time the hardware is evolving with dramatic changes both in processing units and storage devices, where solid-state storage is becoming ubiquitous. In this thesis, we build workload-aware data access methods for data analytics - tailored for emerging time-generated workloads - which use solid-state storage, either (i) as an additional level in the memory hierarchy to enable real-time updates in a data analytics, or (ii) as standalone storage for applications involving support for knowledge-based data, and support for efficiently indexing archival and time-generated data. Building workload-aware and hardware-aware data management systems allows to increase their performance and to augment their functionality. The advancements in storage have led to a variety of storage devices with different characteristics (e.g., monetary cost, access times, durability, endurance, read performance vs. write performance), and the suitability of a method to an application depends on how it balances the different characteristics of the storage medium it uses. The data access methods proposed in this thesis - MaSM and BF-Tree - balance the benefits of solid-state storage and of traditional hard disks, and are suitable for time-generated data or datasets with similar organization, which include social, monitoring and archival applications. The study of work sharing in the context of data analytics paves the way to integrating shared database operators starting from shared scans to several data analytics engines, and the workload-aware physical data organization proposed for knowledge-based datasets - RDF-tuple - enables integration of diverse data sources into the same systems.

EPFL2014

Solid-State Storage and Work Sharing for Efficient Scaleup Data Analytics

Manoussos Gavriil Athanassoulis

EPFL2014