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Wireless Communications and Mobile Computing
Volume 2019, Article ID 4807502, 12 pages
https://doi.org/10.1155/2019/4807502
Research Article

A Selective Mirrored Task Based Fault Tolerance Mechanism for Big Data Application Using Cloud

1School of Software Technology, Dalian University of Technology, Dalian, Liaoning, China
2Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, Guangxi, China

Correspondence should be addressed to Qinggeng Jin; moc.nuyila@gneggniqnij

Received 6 December 2018; Accepted 29 January 2019; Published 26 February 2019

Guest Editor: Salimur Choudhury

Copyright © 2019 Hao Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the wide deployment of cloud computing in big data processing and the growing scale of big data application, managing reliability of resources becomes a critical issue. Unfortunately, due to the highly intricate directed-acyclic-graph (DAG) based application and the flexible usage of processors (virtual machines) in cloud platform, the existing fault tolerant approaches are inefficient to strike a balance between the parallelism and the topology of the DAG-based application while using the processors, which causes a longer makespan for an application and consumes more processor time (computation cost). To address these issues, this paper presents a novel fault tolerant framework named Fault Tolerance Algorithm using Selective Mirrored Tasks Method (FAUSIT) for the fault tolerance of running a big data application on cloud. First, we provide comprehensive theoretical analyses on how to improve the performance of fault tolerance for running a single task on a processor. Second, considering the balance between the parallelism and the topology of an application, we present a selective mirrored task method. Finally, by employing the selective mirrored task method, the FAUSIT is designed to improve the fault tolerance for DAG based application and incorporates two important objects: minimizing the makespan and the computation cost. Our solution approach is evaluated through rigorous performance evaluation study using real-word workflows, and the results show that the proposed FAUSIT approach outperforms existing algorithms in terms of makespan and computation cost.