A Study of Several Matrix-Clustering Vertical Partitioning Algorithms in a Disk-Based Environment

Галактионов, В. А.; Chernishev, George; Smirnov, Kirill; Novikov, Boris; Grigoriev, Dima

doi:10.1007/978-3-319-57135-5_12

Cited by 2 publications

(5 citation statements)

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“…Partitioning of databases, more specifically vertical partitioning has been an active field of research since the 70s and 80s [53,54]. The key idea is to develop an optimization model to satisfy one or more criteria to improve the I/O performance of databases.…”

Section: Multi-tier Storagementioning

confidence: 99%

“…The key idea is to develop an optimization model to satisfy one or more criteria to improve the I/O performance of databases. Partitioning of databases, similar to physical design problems has been proven to be NP-Hard due to the estimation errors in both system and workload parameters [53][54][55], therefore extensive work has been done by the database community [56][57][58][59][60][61][62][63][64].…”

Section: Multi-tier Storagementioning

confidence: 99%

“…All these previous studies by the database community were based on static workloads, which restricts their use for constantly changing workloads [53,54]. Dynamically adaptive variations of these concepts have been explored thoroughly in the design of modern datastores [53,54,61].…”

Section: Multi-tier Storagementioning

confidence: 99%

“…Dynamically adaptive variations of these concepts have been explored thoroughly in the design of modern datastores [53,54,61]. These methods are used in online data partitioning such as O2P, H20 [60], etc., and disk based analytical databases [54,63,65].…”

Section: Multi-tier Storagementioning

confidence: 99%

“…In the Big Data ecosystem, most prominently the concepts of [56][57][58][59] have laid strong footing for the data layout design on HDFS [54,65,66]. Another use case has been in tuning of data stores [53,67], such as a multi-store with HDFS and RDMS together, where every parameter of the datastore is not known apriori. Integration of both horizontal and vertical partitioning together [55] have led to the design of modern Column stores and NoSQL datastores, most popularly, Hbase [23], WideTable [54], RCFile [65], etc.…”

Section: Multi-tier Storagementioning

confidence: 99%

See 4 more Smart Citations

Host managed contention avoidance storage solutions for Big Data

Mishra

Somani

2017

J Big Data

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The performance gap between compute and storage is fairly considerable. This results in a mismatch between the application needs from storage and what storage can deliver. The full potential of storage devices cannot be harnessed till all layers of I/O hierarchy function efficiently. Despite advanced optimizations applied across various layers along the odyssey of data access, the I/O stack still remains volatile. The problems associated due to the inefficiencies in data management get amplified in Big Data shared resource environments. The Linux OS (host) block layer is the most critical part of the I/O hierarchy, as it orchestrates the I/O requests from different applications to the underlying storage. Unfortunately, despite it's significance, the block layer, essentially the block I/O scheduler, hasn't evolved to meet the needs of Big Data. We have designed and developed two contention avoidance storage solutions, collectively known as "BID: Bulk I/O Dispatch" in the Linux block layer specifically to suit multitenant, multi-tasking shared Big Data environments. Hard disk drives (HDDs) form the backbone of data center storage. The data access time in HDDs is majorly governed by disk arm movements, which usually occurs when data is not accessed sequentially. Big Data applications exhibit evident sequentiality but due to the contentions amongst other I/O submitting applications, the I/O accesses get multiplexed which leads to higher disk arm movements. BID schemes aim to exploit the inherent I/O sequentiality of Big Data applications to improve the overall I/O completion time by reducing the avoidable disk arm movements. In the first part, we propose a dynamically adaptable block I/O scheduling scheme BID-HDD for disk based storage. BID-HDD tries to recreate the sequentiality in I/O access in order to provide performance isolation to each I/O submitting process. Through trace driven simulation based experiments with cloud emulating MapReduce benchmarks, we show the effectiveness of BID-HDD which results in 28-52% lesser time for all I/O requests than the best performing Linux disk schedulers. In the second part, we propose a hybrid scheme BID-Hybrid to exploit SCM's (SSDs) superior random performance to further avoid contentions at disk based storage. BID-Hybrid is able to efficiently offload non-bulky interruptions from HDD request queue to SSD queue using BID-HDD for disk request processing and multi-q FIFO architecture for SSD. This results in performance gain of 6-23% for MapReduce workloads when compared to BID-HDD and 33-54% over best performing Linux scheduling scheme. BID schemes as a whole is aimed to avoid contentions for disk based storage I/Os following system constraints without compromising SLAs.

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Section: Multi-tier Storagementioning

confidence: 99%