A survey on bandwidth-aware geo-distributed frameworks for big-data analytics

Mohammed Bergui; Said Najah; Nikola S. Nikolov

doi:10.1186/s40537-021-00427-9

A survey on bandwidth-aware geo-distributed frameworks for big-data analytics

Mohammed Bergui, Said Najah, Nikola S. Nikolov

Department of Computer Science & Information Systems

Sidi Mohamed Ben Abdellah University

Research output: Contribution to journal › Article › peer-review

Abstract

In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.

Original language	English
Article number	40
Journal	Journal of Big Data
Volume	8
Issue number	1
DOIs	https://doi.org/10.1186/s40537-021-00427-9
Publication status	Published - Dec 2021

Keywords

Cloud computing
Geographically distributed clusters
Hadoop
MapReduce
Spark
WAN bandwidth

Access to Document

10.1186/s40537-021-00427-9

Cite this

@article{240b355c977048818284b07e1a188811,

title = "A survey on bandwidth-aware geo-distributed frameworks for big-data analytics",

abstract = "In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.",

keywords = "Cloud computing, Geographically distributed clusters, Hadoop, MapReduce, Spark, WAN bandwidth",

author = "Mohammed Bergui and Said Najah and Nikolov, {Nikola S.}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = dec,

doi = "10.1186/s40537-021-00427-9",

language = "English",

volume = "8",

journal = "Journal of Big Data",

issn = "2196-1115",

number = "1",

}

TY - JOUR

T1 - A survey on bandwidth-aware geo-distributed frameworks for big-data analytics

AU - Bergui, Mohammed

AU - Najah, Said

AU - Nikolov, Nikola S.

PY - 2021/12

Y1 - 2021/12

N2 - In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.

AB - In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.

KW - Cloud computing

KW - Geographically distributed clusters

KW - Hadoop

KW - MapReduce

KW - Spark

KW - WAN bandwidth

UR - http://www.scopus.com/inward/record.url?scp=85101900097&partnerID=8YFLogxK

U2 - 10.1186/s40537-021-00427-9

DO - 10.1186/s40537-021-00427-9

M3 - Article

AN - SCOPUS:85101900097

SN - 2196-1115

VL - 8

JO - Journal of Big Data

JF - Journal of Big Data

IS - 1

M1 - 40

ER -

A survey on bandwidth-aware geo-distributed frameworks for big-data analytics

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this