A distributed large graph coloring algorithm on Giraph

Brighen, Assia; Slimani, Hachem; Rezgui, Abdelmounaam; Kheddouci, Hamamache

doi:10.1109/cloudtech49835.2020.9365872

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…They also proposed the application that can be implemented to develop the application for timetable management [15]. Brighen et al explains the use of distributed approach to solve the GCP for the larger graphs derived from VGCP [16]. They proposed a new algorithm "DistG".…”

Section: Literature Reviewmentioning

confidence: 99%

A Solution to Graph Coloring Problem Using Genetic Algorithm

Malhotra,

Vasa,

Chaudhary

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The Graph Coloring Problem (GCP) involves coloring the vertices of a graph in such a way that no two adjacent vertices share the same color while using the minimum number of colors possible. OBJECTIVES: The main objective of the study is While keeping the constraint that no two neighbouring vertices have the same colour, the goal is to reduce the number of colours needed to colour a graph's vertices. It further investigate how various techniques impact the execution time as the number of nodes in the graph increases. METHODS: In this paper, we propose a novel method of implementing a Genetic Algorithm (GA) to address the GCP. RESULTS: When the solution is implemented on a highly specified Google Cloud instance, we likewise see a significant increase in performance. The parallel execution on Google Cloud shows significantly faster execution times than both the serial implementation and the parallel execution on a local workstation. This exemplifies the benefits of cloud computing for computational heavy jobs like GCP. CONCLUSION: This study illustrates that a promising solution to the Graph Coloring Problem is provided by Genetic Algorithms. Although the GA-based approach does not provide an optimal result, it frequently produces excellent approximations in a reasonable length of time for a variety of real-world situations.

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Section: Literature Reviewmentioning

confidence: 99%

A Solution to Graph Coloring Problem Using Genetic Algorithm

Malhotra,

Vasa,

Chaudhary

et al. 2024

ICST Transactions on Scalable Information Systems

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“…Our study has started from pipelines using analytics graph algorithms to answer community detection problems like page rank, Louvain, more mathematical models applied to matrices representing graphs (run in the LNS, Mexico) according to previous work [2,3]. Our focus will be on the characterization of DS workflows considering (i) the type of graph processing algorithms they address; (ii) the characteristics of the graphs (data) processed and results through these algorithms.…”

Section: Graph Ds Workflows Execution On Disaggregated Data Centresmentioning

confidence: 99%

Graph Analytics Workflows Enactment on Just in Time Data Centres Position Paper

Akoglu

Zechinelli-Martini

Kheddouci

et al. 2022

Service-Oriented Computing – ICSOC 2021 Workshops

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This paper discusses our vision about multirole-capable decisionmaking systems across a broad range of Data Science (DS) workflows working on graphs through disaggregated data centres. Our vision is that an alternative is possible, to work on a disaggregated solution for the provision of computational services under the notion of a disaggregated data centre. We define this alternative as a virtual entity that dynamically provides resources crosscutting the layers of edge, fog and data centre according to the workloads submitted by the workflows and their Service Level Objectives. This work is funded by the project GALILEAN, LIRIS intergroup collaboration https://galilean-project.github.io.

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