Parallel and distributed computing for Big Data applications

Author:

Senger Hermes1,Geyer Claudio2

Affiliation:

1. Universidade Federal de São Carlos (UFSCar); São Carlos SP Brazil

2. Universidade Federal University do Rio Grande do Sul (UFRGS); Porto Alegre RS Brazil

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference9 articles.

1. Big data and its technical challenges;Jagadish;Communications of the ACM,2014

2. BIGhybrid: a simulator for MapReduce applications in hybrid distributed infrastructures validated with the Grid5000 experimental platform;Anjos;Concurrency and Computation: Practice and Experience,2015

3. A generic API for load balancing in distributed systems for big data management;Antoine;Concurrency and Computation: Practice and Experience,2015

4. Automatic I/O scheduling algorithm selection for parallel file systems;Boito;Concurrency and Computation: Practice and Experience,2015

5. Selection and replacement algorithms for memory performance improvement in spark;Duan;Concurrency and Computation: Practice and Experience,2015

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2. Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data;Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications;2018

3. Computational Performance Analysis of Cluster-based Technologies for Big Data Analytics;2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData);2017-06

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