AptStore: Dynamic storage management for hadoop

Krish, K. R.; Khasymski, Aleksandr; Butt, Ali R.; Tiwari, Sameer; Bhandarkar, Milind

doi:10.1109/cluster.2013.6702657

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…DW methodologies are for numeric data (e.g., sales activity), they are inappropriate for new data structures and requirements. Indeed, they are inefficient for building/handling huge data volumes (Krish, et al, 2013;Chevalier et al, 2015a).…”

Section: Approach For Building a Document-oriented Nosql Warehousementioning

confidence: 99%

“…Over the past decade, several digital players (e.g., sensors, social networks) produce unlimited amounts of data so that the data volumes to analyze reach critical sizes (Jacobs, 2009). Nevertheless, current warehousing methodologies become obsolete to handle successfully the growing data volumes as stated in (Krish et al, 2013) and (Chevalier et al, 2015a). To overcome this drawback, NoSQL (Not-Only SQL) appear as a new technology to implement huge databases and, in particular, document warehouses.…”

Section: Introductionmentioning

confidence: 99%

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Building a Document-Oriented Warehouse Using NoSQL

Messaoud

Alshdadi

Feki

2021

International Journal of Operations Research and Information Systems

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The traditional data warehousing approaches should adapt to take into consideration novel needs and data structures. In this context, NoSQL technology is progressively gaining a place in the research and industry domains. This paper proposes an approach for building a NoSQL document-oriented warehouse (DocW). This approach has two methods, namely 1) document warehouse builder and 2) NoSQL-Converter. The first method generates the DocW schema as a galaxy model whereas the second one translates the generated galaxy into a document-oriented NoSQL model. This relies on two types of rules: structure and hierarchical rules. Furthermore, in order to help understanding the textual results of analytical queries on the NoSQL-DocW, the authors define two semantic operators S-Drill-Up and S-Drill-Down to aggregate/expand the terms of query. The implementation of our proposals uses MangoDB and Talend. The experiment uses the medical collection Clef-2007 and two metrics called write request latency and read request latency to evaluate respectively the loading time and the response time to queries.

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Section: Approach For Building a Document-oriented Nosql Warehousementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building a Document-Oriented Warehouse Using NoSQL

Messaoud

Alshdadi

Feki

2021

International Journal of Operations Research and Information Systems

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“…For example, partial fault tolerance was shown to be highly effective in [17]. Another angle (for example, [21]) is to have a non-static number of replicas in the conventional wisdom, so that more replicas could support higher throughput for popular data and fewer replicas are allocated for unpopular data to save the storage cost. In distributed networks, location-aware replication and independent service-structure failure recovery was proposed for group communication services in [40].…”

Section: Related Workmentioning

confidence: 99%

Toward high-performance key-value stores through GPU encoding and locality-aware encoding

Zhao

Wang

Qiao

et al. 2016

Journal of Parallel and Distributed Computing

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Although distributed key-value store is becoming increasingly popular in compensating the conventional distributed file systems, it is often criticized due to its costly full-size replication for high availability that causes high I/O overhead. This paper presents two techniques to mitigate such I/O overhead and improve key-value store performance: GPU encoding and locality-aware encoding. Instead of migrating full-size replicas over the network, we split the original file into smaller chunks and encode them with a few additional parity codes using GPUs before dispersing them onto remote nodes. The parity code is usually much smaller than the original file, which saves the extra space required for high availability and reduces the I/O overhead. Meanwhile, the compute-intensive encoding process is largely accelerated by the massive number of GPU cores. Yet, splitting the original file into smaller chunks stored on multiple nodes breaks data locality from application's perspective. To this end, we present a locality-aware encoding mechanism that allows a job to be dispatched as finer-grained tasks right on the node where the required chunk resides. Therefore, the data locality is preserved at the finer granularity of sub-job (i.e., task) level. We conduct an in-depth analysis of the proposed approach and implement a system prototype named Gest. Gest has been deployed and evaluated on a variety of testbeds demonstrating that high data availability, high space efficiency, and high I/O performance could be collectively achieved at the same time.

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