A survey: benchmarking and performance modelling of data intensive applications

Sheriffo Ceesay; Yuhui Lin; Adam David Barker

doi:10.1109/BDCAT50828.2020.00012

A survey: benchmarking and performance modelling of data intensive applications

Sheriffo Ceesay, Yuhui Lin^*, Adam David Barker^*

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends

Original language	English
Title of host publication	2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
Publisher	IEEE
Pages	67-76
Number of pages	10
ISBN (Electronic)	9780738123967
ISBN (Print)	9781665415675
DOIs	https://doi.org/10.1109/BDCAT50828.2020.00012
Publication status	Published - 7 Dec 2020
Event	7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2020) - University of Leicester, Leicester, United Kingdom Duration: 7 Dec 2020 → 12 Dec 2020 Conference number: 7 https://www.cs.le.ac.uk/events/BDCAT2020/index.htm

Conference

Conference	7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2020)
Abbreviated title	BDCAT 2020
Country/Territory	United Kingdom
City	Leicester
Period	7/12/20 → 12/12/20
Internet address	https://www.cs.le.ac.uk/events/BDCAT2020/index.htm

Keywords

Dataflow with cycles
Communication patterns
Modelling
Machine learning
Big data
Data-intensive application

Access to Document

10.1109/BDCAT50828.2020.00012

Cite this

@inproceedings{112bbf6ae27b4e8c83b7e98a7d22b9ee,

title = "A survey: benchmarking and performance modelling of data intensive applications",

abstract = "In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends",

keywords = "Dataflow with cycles, Communication patterns, Modelling, Machine learning, Big data, Data-intensive application",

author = "Sheriffo Ceesay and Yuhui Lin and Barker, {Adam David}",

year = "2020",

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day = "7",

doi = "10.1109/BDCAT50828.2020.00012",

language = "English",

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booktitle = "2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)",

publisher = "IEEE",

note = "7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2020), BDCAT 2020 ; Conference date: 07-12-2020 Through 12-12-2020",

url = "https://www.cs.le.ac.uk/events/BDCAT2020/index.htm",

}

Ceesay, S, Lin, Y & Barker, AD 2020, A survey: benchmarking and performance modelling of data intensive applications. in 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)., 9302534, IEEE, pp. 67-76, 7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2020), Leicester, United Kingdom, 7/12/20. https://doi.org/10.1109/BDCAT50828.2020.00012

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AU - Lin, Yuhui

AU - Barker, Adam David

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N2 - In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends

AB - In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends

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