NAND flash memory (hereafter, flash memory) has been intensively employed in a wide spectrum of computing systems from mobile devices like smartphones to personal computers to enterprise servers due to its high performance, low power consumption, and shock resistance. However, the further deployment of flash memory is impeded because it also possesses several inherent disadvantages such as limited programming/erase cycles and asymmetrical I/O performance. Besides, the existing frameworks for storage systems are originally designed for block devices (e.g., hard disk drives), which have totally different characteristics from flash memory. In order to utilize flash memory in current storage systems, an extra software layer between a traditional storage system interface and flash memory is needed to mimic the behavior of a block device. Unfortunately, using a flash-based storage system as a traditional HDD noticeably neutralizes the benefits of flash memory.In this dissertation, we holistically examine current flash-based storage systems in different computing platforms ranging from embedded systems to enterprise servers. Firstly, we empirically characterize a representative collection of flash memory devices and then model their raw I/O performance and reliability. Our results demonstrate that flash memory performance and reliability are correlated to programmed data patterns. Further, we propose multiple approaches to improving the performance and reliability of flash-based storage systems at device level. Secondly, we study flash translation layer (FTL) in flash-based solid-state drives (SSDs) for desktops. A plane-centric FTL and a workload-aware MLC/SLC (multi-level cell/single-level cell) partitioning scheme are implemented to boost the performance of a single SSD. Thirdly, the employment of SSD arrays in enterprise servers is investigated. We propose a load-balancing scheme at disk array level to prolong the lifetime of SSD arrays for server applications like OLTP (online transaction processing). Finally, an MTD (memory technology device) array based storage framework will be developed to meet the performance and reliability requirements demanded by emerging and future data-intensive and mission-critical mobile applications.

High-density NAND flash storage has become relatively inexpensive due to the popularity of various consumer electronics. Recently, several manufacturers have released IDE-compatible NAND flash-based drives in sizes up to 64 GB at reasonable (sub-$1000) prices. Because flash is significantly more durable than mechanical hard drives and requires considerably less energy, there is some speculation that large data centers will adopt these devices. As database workloads make up a substantial fraction of the processing done by data centers, it is interesting to ask how switching to flash-based storage will affect the performance of database systems. We evaluate this question using IDE-based flash drives from two major manufacturers. We measure their read and write performance and find that flash has excellent random read performance, acceptable sequential read performance, and quite poor write performance compared to conventional IDE disks. We then consider how standard database algorithms are affected by these performance characteristics and find that the fast random read capability dramatically improves the performance of secondary indexes and index-based join algorithms. We next investigate using logstructured filesystems to mitigate the poor write performance of flash and find an 8.2x improvement in random write performance, but at the cost of a 3.7x decrease in random read performance. Finally, we study techniques for exploiting the inherent parallelism of multiple-chip flash devices, and we find that adaptive coding strategies can yield a 2x performance improvement over static ones. We conclude that in many cases flash disk performance is still worse than on traditional drives and that current flash technology may not yet be mature enough for widespread database adoption if performance is a dominant factor. Finally, we briefly speculate how this landscape may change based on expected performance of next-generation flash memories.

The four-volume set LNCS 11334-11337 constitutes the proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018, held in Guangzhou, China, in November 2018. The 141 full and 50 short papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on Distributed and Parallel Computing; High Performance Computing; Big Data and Information Processing; Internet of Things and Cloud Computing; and Security and Privacy in Computing.

4 zettabytes (4 billion terabytes) of data generated in 2013, 44 zettabytes predicted for 2020 and 185 zettabytes for 2025. These figures are staggering and perfectly illustrate this new era of data deluge. Data has become a major economic and social challenge. The speed of processing of these data is the weakest link in a computer system: the storage system. It is therefore crucial to optimize this operation. During the last decade, storage systems have experienced a major revolution: the advent of flash memory. Flash Memory Integration: Performance and Energy Issues contributes to a better understanding of these revolutions. The authors offer us an insight into the integration of flash memory in computer systems, their behavior in performance and in power consumption compared to traditional storage systems. The book also presents, in their entirety, various methods for measuring the performance and energy consumption of storage systems for embedded as well as desktop/server computer systems. We are invited on a journey to the memories of the future. - Ideal for computer scientists, featuring low level details to concentrate on system issues - Tackles flash memory aspects while spanning domains such as embedded systems and HPC - Contains an exhaustive set of experimental results conducted in the Lab-STICC laboratory - Provides details on methodologies to perform performance and energy measurements on flash storage systems

NAND flash memory is playing a key role in the revolution of storage systems due to its desirable features such as fast random read and high energy-efficiency. It has been extensively applied in mobile devices like smart phones and PDAs. With increasing capacity, throughput and durability, NAND flash memory based solid state disk (hereafter, flash SSD) has started replacing hard disk drive (HDD) in laptops and desktop systems. Employing high-end flash SSDs in server applications, however, is promising yet challenging. One of the challenges is that currently flash SSD cannot fully meet heavy random write requirements demanded by data-intensive enterprise applications like online transaction processing (OLTP) because of flash memory's inherent update/erasure mechanisms. In this thesis, to boost flash SSD random write performance, we develop a new cache management scheme called element-level parallel optimization (EPO), which buffers and reorders write requests so that element-level parallelism within the architecture of a flash SSD can be mostly utilized. Further, we evaluate the performance of the EPO scheme using a validated disk simulator with both synthetic benchmarks and real-world server-class traces. Experimental results demonstrate that EPO noticeably outperforms traditional least recently used (LRU) and a state-of-the-art flash write buffer management scheme block padding least recently used (BPLRU).

Abstract: As an emerging storage technology, Flash Memory based Solid State Drive (SSD) has shown a high potential to fundamentally change the existing Hard Disk Drive (HDD) based storage systems. Unlike conventional magnetic disks, SSD is built on semiconductor chips and has no mechanical components (e.g. rotating disk platters). This architectural difference brings many attractive technical features, such as high random data access performance and low power consumption. Most importantly, these unique features could potentially address the long-existing technical limitations of conventional magnetic disks. Due to this reason, SSD has been called a 'pivotal technology' that may completely revolutionize current computer storage systems.