<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by <xref ref-type="bibr" rid="j_cait-2018-0031_ref_032_w2aab3b7b7b1b6b1ab1ac32Aa">Xin-She Yang (2010)</xref>. This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.</p></abstract>

In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

School of Information Technology and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data"}

In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely...

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Pubblicato online: 30 giu 2018

Pagine: 98 - 111

Ricevuto: 14 dic 2017

Accettato: 02 giu 2018

DOI: https://doi.org/10.2478/cait-2018-0031

Parole chiave
Map reduce framework, task scheduling, firefly algorithm, BAT Algorithm, Hadoop, k-Means clustering, Hadoop, HDFS

© 2018 M. Senthilkumar, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Pubblicato online: 30 giu 2018

Pagine: 98 - 111

Ricevuto: 14 dic 2017

Accettato: 02 giu 2018

DOI: https://doi.org/10.2478/cait-2018-0031

Parole chiaveMap reduce framework, task scheduling, firefly algorithm, BAT Algorithm, Hadoop, k-Means clustering, Hadoop, HDFS

© 2018 M. Senthilkumar, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Map reduce framework, task scheduling, firefly algorithm, BAT Algorithm, Hadoop, k-Means clustering, Hadoop, HDFS