Memory efficient and scalable address mapping for flash storage devices

Suh, Young-Kyoon; Moon, Bongki; Efrat, Alon; Lee, Sang Won

doi:10.1016/j.sysarc.2014.01.002

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…We thus can infer that reducing I/O overhead can be a critical factor in preventing transaction throughput from falling off at a lower MPL. DBAs therefore may consider several approaches to improve I/O, including 1) increasing physical memory, 2) creating secondary indexes, 3) enabling caches from disk drive through file system up to DBMS buffer, and 4) replacing traditional disks with solid-state drives (SSDs) for reducing access latency [60]. These approaches can delay thrashing, or perhaps even prevent it in some situations.…”

Section: Engineering Implicationsmentioning

confidence: 99%

An empirical study of transaction throughput thrashing across multiple relational DBMSes

Suh

Snodgrass

Currim

2017

Information Systems

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Modern DBMSes are designed to support many transactions running simultaneously. DBMS thrashing is indicated by the existence of a sharp drop in transaction throughput. Thrashing behavior in DBMSes is a serious concern to database administrators (DBAs) as well as to DBMS implementers. From an engineering perspective, therefore, it is of critical importance to understand the causal factors of DBMS thrashing. However, understanding the origin of thrashing in modern DBMSes is challenging, due to many factors that may interact with each other. This article aims to better understand the thrashing phenomenon across multiple DBMSes. We identify some of the underlying causes of DBMS thrashing. We then propose a novel structural causal model to explicate the relationships between various factors contributing to DBMS thrashing. Our model derives a number of specific hypotheses to be subsequently tested across DBMSes, providing empirical support for this model as well as important engineering implications for improvements in transaction processing.

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Section: Engineering Implicationsmentioning

confidence: 99%

An empirical study of transaction throughput thrashing across multiple relational DBMSes

Suh

Snodgrass

Currim

2017

Information Systems

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“…In this regard, we propose a novel spatiotemporal indexing scheme, termed ST-Trie, to gain efficient access to heterogeneous IoT sensor data stored in a NoSQL database (MongoDB [21]). The key ideas are to first convert three-dimensional spatial and temporal data to one-dimensional index keys expressed by 64 bit integers (while preserving their locality) and to organize the encoded integer values into a (virtual) trie (the latter idea was inspired by some previous works [22,23] that achieved efficient retrieval in flash memory page address mapping). Unlike existing schemes whose performance depends on the number of stored nodes, our trie-based indexing scheme reaches a target node in constant time, regardless of how many nodes exist in the trie.…”

Section: Introductionmentioning

confidence: 99%

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Chu

Kim

et al. 2020

Sustainability

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Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

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Virtual duplication and mapping prefetching for emerging storage primitives in NAND flash memory storage systems

Wang

Dong

Ming

et al. 2017

Microprocessors and Microsystems

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Memory efficient and scalable address mapping for flash storage devices

Cited by 3 publications

References 8 publications

An empirical study of transaction throughput thrashing across multiple relational DBMSes

An empirical study of transaction throughput thrashing across multiple relational DBMSes

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Virtual duplication and mapping prefetching for emerging storage primitives in NAND flash memory storage systems

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