SDN helps Big Data to become fault tolerant

2018 ◽  
pp. 319-336
Keyword(s):  
Big Data ◽  
Fault Tolerant

scholarly journals Building a fault tolerant framework with deadline guarantee in big data stream computing environments

2017 ◽  
Vol 89 ◽  
pp. 4-23 ◽  
Author(s):  
Dawei Sun ◽  
Guangyan Zhang ◽  
Chengwen Wu ◽  
Keqin Li ◽  
Weimin Zheng
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Fault Tolerant ◽  
Stream Computing ◽  
Computing Environments

Mobile big data fault-tolerant processing for ehealth networks

IEEE Network ◽  
2016 ◽  
Vol 30 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Kun Wang ◽  
Yun Shao ◽  
Lei Shu ◽  
Chunsheng Zhu ◽  
Yan Zhang
Keyword(s):  
Big Data ◽  
Fault Tolerant ◽  
Mobile Big Data

scholarly journals HEAP: An Efficient and Fault-Tolerant Authentication and Key Exchange Protocol for Hadoop-Assisted Big Data Platform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75342-75382 ◽  
Author(s):  
Durbadal Chattaraj ◽  
Monalisa Sarma ◽  
Ashok Kumar Das ◽  
Neeraj Kumar ◽  
Joel J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Big Data ◽  
Fault Tolerant ◽  
Key Exchange ◽  
Key Exchange Protocol ◽  
Data Platform

scholarly journals SCBI_MapReduce, a New Ruby Task-Farm Skeleton for Automated Parallelisation and Distribution in Chunks of Sequences: The Implementation of a Boosted Blast+

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Darío Guerrero-Fernández ◽  
Juan Falgueras ◽  
M. Gonzalo Claros
Keyword(s):  
Big Data ◽  
Virtual Machine ◽  
Fault Tolerant ◽  
Source Code ◽  
Proof Of Concept ◽  
Shared Storage ◽  
Legacy Software ◽  
Genomic Analyses ◽  
Speed Up ◽  
Legacy Applications

Current genomic analyses often require the managing and comparison of big data using desktop bioinformatic software that was not developed regarding multicore distribution. The task-farm SCBI_MAPREDUCE is intended to simplify the trivial parallelisation and distribution of new and legacy software and scripts for biologists who are interested in using computers but are not skilled programmers. In the case of legacy applications, there is no need of modification or rewriting the source code. It can be used from multicore workstations to heterogeneous grids. Tests have demonstrated that speed-up scales almost linearly and that distribution in small chunks increases it. It is also shown that SCBI_MAPREDUCE takes advantage of shared storage when necessary, is fault-tolerant, allows for resuming aborted jobs, does not need special hardware or virtual machine support, and provides the same results than a parallelised, legacy software. The same is true for interrupted and relaunched jobs. As proof-of-concept, distribution of a compiled version of BLAST+ in the SCBI_DISTRIBUTED_BLAST gem is given, indicating that other blast binaries can be used while maintaining the same SCBI_DISTRIBUTED_BLAST code. Therefore, SCBI_MAPREDUCE suits most parallelisation and distribution needs in, for example, gene and genome studies.

scholarly journals Analysis and Experimental Study of HDFS Performance

TEM Journal ◽  
2021 ◽  
pp. 806-814
Author(s):  
Yordan Kalmukov ◽  
Milko Marinov ◽  
Tsvetelina Mladenova ◽  
Irena Valova
Keyword(s):  
Experimental Study ◽  
Big Data ◽  
Computer System ◽  
Data Storage ◽  
Fault Tolerant ◽  
Processing System ◽  
Daily Basis ◽  
Distributed File System ◽  
Hadoop Distributed File System ◽  
Big Data Storage

In the age of big data, the amount of data that people generate and use on a daily basis has far exceeded the storage and processing capabilities of a single computer system. That motivates the use of distributed big data storage and processing system such as Hadoop. It provides a reliable, horizontallyscalable, fault-tolerant and efficient service, based on the Hadoop Distributed File System (HDFS) and MapReduce. The purpose of this research is to experimentally determine whether (and to what extent) the network communication speed, the file replication factor, the files’ sizes and their number, and the location of the HDFS client influence the performance of the HDFS read/write operations.

scholarly journals Coming Together of Big Data and Cloud Computing : A Review

2020 ◽  
pp. 118-137
Author(s):  
Muneeba Afzal Mukhdoomi ◽  
Ashish Oberoi ◽  
Ankur Gupta
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Fault Tolerant ◽  
The Other ◽  
Data Sets ◽  
Application Software ◽  
Data Handling ◽  
Computing Power ◽  
Rapid Pace ◽  
Tools And Techniques

Big data stands for sheer amount of data that is growing unceasingly at a rapid pace. Big Data demands high-powered, robust, reliable, fault-tolerant tools and techniques in order to make it convenient to process, analyse and uproot new insights from Big Data. Big data refers to huge, heterogeneous amount of details, facts and data generating at constantly rising rate. The data sets in Big Data are too bulky or extensive, as a result classical data handling application software are not competent enough to administer them. On the other hand, Cloud computing is a resourceful technology providing high computing power, scalability, computing resources as and when required for processing, storage, analytics and visualization of Big Data. Therefore, cloud computing can be regarded as a feasible and applicable technology which promises to handle Big Data challenges and also provides here and now infrastructures with all the mandatory resources. This paper will mainly review processing of big data cloud using Hadoop and spark in cloud, advantages of driving Big Data using cloud computing and applications of Big data in Cloud.

scholarly journals FTLADS: Object-Logging Based Fault-Tolerant Big Data Transfer System Using Layout Aware Data Scheduling

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37448-37462
Author(s):  
Preethika Kasu ◽  
Taeuk Kim ◽  
Jung-Ho Um ◽  
Kyongseok Park ◽  
Scott Atchley ◽  
...  
Keyword(s):  
Big Data ◽  
Data Transfer ◽  
Fault Tolerant ◽  
Transfer System ◽  
Data Scheduling
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