Introducing TPCx-HS: The First Industry Standard for Benchmarking Big Data Systems

Nambiar, Raghunath; Poess, Meikel; Dey, Akon; Cao, Paul; Magdon-Ismail, Tariq; Ren, Da Qi; Bond, A. J.

doi:10.1007/978-3-319-15350-6_1

Cited by 23 publications

(7 citation statements)

References 2 publications

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“…It gives an objective measurement of the system being tested and through this, the industry can compare multiple existing solutions based on several metrics. The TPCx‐HS benchmark focuses on testing commercialised Apache Hadoop systems [54, 55]. The testing covers both the hardware and software aspects of the system, which includes the operating system.…”

Section: Emerging and Existing Big Data Standardsmentioning

confidence: 99%

“…A CIM RDF schema proposed in the IEC 61970‐501 standard is used to construct CML documents with power system model information, so that they can be exchanged. Key Takeaway Points: TPC benchmarks can be considered in tune with CMMs. They are application benchmarks for big data towards an industry‐standard benchmark for big data analytics. The authors of [47, 48, 51] cover various aspects of data security and are adaptable to all DG use‐cases, as described later in this paper. Consortia such as the Organization for the Advancement of Structured Information Standards (OASIS), the World Wide Web Consortium (W3C), the Open Geospatial Consortium (OGC), the Open GRASS Foundation (OGF), and others have created standards for data, cited to have relevance to big data. The authors of [50, 52] provide an overview of understanding and creating big data projects. TPC Benchmarks [53–55] might provide better insight into the operation of big data analytics systems and might help users re‐evaluate their implemented models. …”

Section: Emerging and Existing Big Data Standardsmentioning

confidence: 99%

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Adapting big data standards, maturity models to smart grid distributed generation: critical review

2020

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Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG. 3 Emerging and existing big data standards Key big data standards are described below, their relevance to attributes and lifecycle stages summarised in Table 2.

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Section: Emerging and Existing Big Data Standardsmentioning

confidence: 99%

Section: Emerging and Existing Big Data Standardsmentioning

confidence: 99%

Adapting big data standards, maturity models to smart grid distributed generation: critical review

2020

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“…Another benchmark is the TPCx-HS that adds formal specification and enforcement rules that enable comparison of results among systems [35]. One characteristic of TPCx-HS is that it follows a stepped scaled sizing model on BigdataBench.…”

Section: Big-data Benchmarksmentioning

confidence: 99%

An Efficient Industrial Big-Data Engine

Val

2018

IEEE Trans. Ind. Inf.

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Abstract-Current trends in industrial systems opt for the use of different big-data engines as a mean to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, the article explores the development of a medium size big-data engine (i.e. implementation) able to improve performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases performance of current clusters improving I/O operations remarkably as empirical results revealed.

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“…It is considered that the data scale that was used in this study of up to 2 TB was a good indicator that KERMIT can perform efficiently in real-world big data applications. This was one of the team's goals because MRONLINE [25], the other research project that dealt with automatic tuning of Hadoop/YARN, used data sizes of a maximum of 100 GB; this would be considered small if compared to leading benchmarks like TPCx-HS [54] that have 1 TB as the smallest data scale allowed for that specific benchmark. The data scale is of particular interest; the results at different data scales are not comparable to each other because the computational challenges can vary considerably at different data volumes.…”

Section: Results Analysismentioning

confidence: 99%

“…It was inspired by the TeraSort/TeraGen/TeraValidate utilities that have been commonly used by big data practitioners for several years. TPCx-HS requires that the generate, sort and validate jobs sequence is executed sequentially without interruptions [54]. It specifically disallows manual tuning between the benchmark stages, but it explicitly allows for automatic tuning.…”

Section: Hadoop Benchmarksmentioning

confidence: 99%

Improving Real Time Tuning on YARN

Belmonte¹,

Pablo²

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Big data is becoming a significant part of the operations of modern organizations. Performing analysis of large amounts of data requires computer clusters to run the calculations and analysis. YARN is an internal framework that is responsible for coordinating big data jobs for some popular distributed storage and processing frameworks like Hadoop and Spark. Running YARN with the correct configuration parameters is critical for the good performance of a cluster. KERMIT is an online tuner of YARN configuration parameters that aims to improve cluster performance. The first KER-MIT implementation proved the feasibility of the concept. In this study we modified the tuning algorithms inside the KERMIT components; by doing so, we achieved a reduction in the execution time as compared to industry benchmarks for a shallow tuning technique. We also verified that KERMIT can tune Spark; this suggests that Kermit could be used in other YARN-based frameworks.

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Introducing TPCx-HS: The First Industry Standard for Benchmarking Big Data Systems

Cited by 23 publications

References 2 publications

Adapting big data standards, maturity models to smart grid distributed generation: critical review

Adapting big data standards, maturity models to smart grid distributed generation: critical review

An Efficient Industrial Big-Data Engine

Improving Real Time Tuning on YARN

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