Authors :
Lacine KABRE; Telesphore TIENDREBEOGO
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/3f9x8cy6
Scribd :
http://tinyurl.com/2p96cdat
DOI :
https://doi.org/10.5281/zenodo.10375062
Abstract :
In massive data processing, platforms using
MapReduce are designed for data centers, which are
generally centralized.These platforms typically rely on a
single node to maintain and coordinate MapReduce
tasks, leading to a single point of failure. Our aim in this
paper has been to propose a model for MapReduce
computation on the Red Green Blue architecture, which
is a decentralized, triple-node big data architecture.
This architecture is based on the peer-to-peer
networking protocol named Content Addressable
Network. First, we implemented all the steps of the
MapReduce computation approach, taking into account
the properties of the Content Addressable Network
protocol and the Red Green Bluearchitecture. We then
carried out an experiment in a local network to evaluate
performance in terms of processing speed and time. The
experiment showed that latency decreased with the
number of compute nodes. This study not only showed
that the Red Green Blue architecture is viable as a
massive data processing architecture, but also improved
processing times as a function of network nodes. The
robustness, scalability and lack of a single point of
failure of the Red Green Bluearchitecture mean that
MapReduce can be easily deployed in a wider variety of
applications.
Keywords :
P2P protocol, Map Reduce, RGB architecture, Big data Storage.
In massive data processing, platforms using
MapReduce are designed for data centers, which are
generally centralized.These platforms typically rely on a
single node to maintain and coordinate MapReduce
tasks, leading to a single point of failure. Our aim in this
paper has been to propose a model for MapReduce
computation on the Red Green Blue architecture, which
is a decentralized, triple-node big data architecture.
This architecture is based on the peer-to-peer
networking protocol named Content Addressable
Network. First, we implemented all the steps of the
MapReduce computation approach, taking into account
the properties of the Content Addressable Network
protocol and the Red Green Bluearchitecture. We then
carried out an experiment in a local network to evaluate
performance in terms of processing speed and time. The
experiment showed that latency decreased with the
number of compute nodes. This study not only showed
that the Red Green Blue architecture is viable as a
massive data processing architecture, but also improved
processing times as a function of network nodes. The
robustness, scalability and lack of a single point of
failure of the Red Green Bluearchitecture mean that
MapReduce can be easily deployed in a wider variety of
applications.
Keywords :
P2P protocol, Map Reduce, RGB architecture, Big data Storage.
