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.

This book provides basic knowledge about main memory management in relational databases as it is needed to support large-scale applications processed completely in memory. In business operations, real-time predictability and high speed is a must. Hence every opportunity must be exploited to improve performance, including reducing dependency on the hard disk, adding more memory to make more data resident in the memory, and even deploying an in-memory system where all data can be kept in memory. The book provides one chapter for each of the main related topics, i.e. the memory system, memory management, virtual memory, and databases and their memory systems, and it is complemented by a short survey of six commercial systems: TimesTen, MySQL, VoltDB, Hekaton, HyPer/ScyPer, and SAP HANA.

This book constitutes the refereed proceedings of the 8th International Symposium on Experimental and Efficient Algorithms, SEA 2009, held in Dortmund, Germany, in June 2009. The 23 revised full papers were carefully reviewed and selected from 64 submissions and present current research on experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. Contributions are supported by experimental evaluation, methodological issues in the design and interpretation of experiments, the use of (meta- ) heuristics, or application-driven case studies that deepen the understanding of a problem's complexity.

This book constitutes the proceedings of the 18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021, which was held in Paris, France during November 3-5, 2021. The 20 papers presented in this volume were carefully reviewed and selected from 62 submissions. They were organized in topical sections as follows: algorithms and applications; system software and resource management; storage; and networks and communications.

This book constitutes the workshop proceedings of the 16th International Conference on Database Systems for Advanced Applications, DASFAA 2011, held in Hong Kong, China, in April 2011. The volume contains six workshops, each focusing on specific research issues that contribute to the main themes of the DASFAA conference: The First International Workshop on Graph-structured Data Bases (GDB 2011); the First International Workshop on Spatial Information Modeling, Management and Mining (SIM3 2011); the International Workshop on Flash-based Database Systems (FlashDB 2011); the Second International Workshop on Social Networks and Social Media Mining on the Web (SNSMW 2011); the First International Workshop on Data Management for Emerging Network Infrastructures (DaMEN 2011); and the Fourth International Workshop on Data Quality in Integration Systems (DQIS 2011).

Digital photography, MP3, digital video, etc. make extensive use of NAND-based Flash cards as storage media. To realize how much NAND Flash memories pervade every aspect of our life, just imagine how our recent habits would change if the NAND memories suddenly disappeared. To take a picture it would be necessary to find a film (as well as a traditional camera...), disks or even magnetic tapes would be used to record a video or to listen a song, and a cellular phone would return to be a simple mean of communication rather than a multimedia console. The development of NAND Flash memories will not be set down on the mere evolution of personal entertainment systems since a new killer application can trigger a further success: the replacement of Hard Disk Drives (HDDs) with Solid State Drives (SSDs). SSD is made up by a microcontroller and several NANDs. As NAND is the technology driver for IC circuits, Flash designers and technologists have to deal with a lot of challenges. Therefore, SSD (system) developers must understand Flash technology in order to exploit its benefits and countermeasure its weaknesses. Inside NAND Flash Memories is a comprehensive guide of the NAND world: from circuits design (analog and digital) to Flash reliability (including radiation effects), from testing issues to high-performance (DDR) interface, from error correction codes to NAND applications like Flash cards and SSDs.

The non-volatile memory technique advanced rapidly in recent years. First, mature NAND flash memory is getting cheaper and denser. It has impacted our daily life. Second, emerging persistent memory technologies such as 3d XPoint have demonstrated great potentials in revolutionizing modern memory hierarchy. In this research, we first carry out a project on the mature NAND-flash-based solid state drives. We propose a new RAID5 technique called CR5M to enhance data reliability within a single SSD for safety-critical mobile applications. We also proposed an associated data reconstruction strategy called MCR to further shrink the window of vulnerability. Compared with traditional RAID5, CR5M can achieve up to 40.2% performance improvement. The data recovery speed is also improved by 7.5%. Because persistent memory is byte-addressable and has near-DRAM access speed, it exhibits a huge potential to build a hybrid memory system where both DRAM and PM are directly connected to a CPU. We designed a concurrent hash-assisted radix tree for DRAM-PM Hybrid Memory Systems. In such a system, an efficient indexing data structure such as a persistent tree becomes an indispensable component. Designing a capable persistent tree, however, is challenging as it has to ensure consistency, persistence, and scalability without substantially degrading performance. We propose a novel concurrent and persistent tree called HART (Hash-assisted ART), which employs a hash table to manage ARTs. HART not only optimize its performance but also prevent persistent memory leaks. In most cases, HART significantly outperforms WOART and FPTree, two state-of-the-art persistent trees. Also, it scales well in concurrent scenarios. Then, we proposed multi-hashing, a dual-level hash table indexing for a highperformance, large-capacity, and low-cost in-memory database. Multi-hashing is also built on a DRAM-PM hybrid memory system. On the DRAM level, an indexing structure is designed to be memory-efficient to manage hot indexes. On the PM level, another indexing data structure is designed to be performance-optimized. The indexes in DRAM will be merged into PM periodically. Our experimental results show that multi-hashing shows better performance under Sparse workloads when compared with HART. It also consumes less memory under both Dense and Sparse workloads.