iiHadoop: an asynchronous distributed framework for incremental iterative computations
Abstract It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new systems to perform large-scale data computations incrementally. MapReduce was rec...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2017-07-01
|
Series: | Journal of Big Data |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s40537-017-0086-3 |
_version_ | 1811265201387864064 |
---|---|
author | Afaf G. Bin Saadon Hoda M. O. Mokhtar |
author_facet | Afaf G. Bin Saadon Hoda M. O. Mokhtar |
author_sort | Afaf G. Bin Saadon |
collection | DOAJ |
description | Abstract It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new systems to perform large-scale data computations incrementally. MapReduce was recently introduced to provide an efficient approach for handling large-scale data computations. Nevertheless, it turned to be inefficient in supporting the processing of small incremental data. While many previous systems have extended MapReduce to perform iterative or incremental computations, these systems are still inefficient and too expensive to perform large-scale iterative computations on changing data. In this paper, we present a new system called iiHadoop, an extension of Hadoop framework, optimized for incremental iterative computations. iiHadoop accelerates program execution by performing the incremental computations on the small fraction of data that is affected by changes rather than the whole data. In addition, iiHadoop improves the performance by executing iterations asynchronously, and employing locality-aware scheduling for the map and reduce tasks taking into account the incremental and iterative behavior. An evaluation for the proposed iiHadoop framework is presented using examples of iterative algorithms, and the results showed significant performance improvements over comparable existing frameworks. |
first_indexed | 2024-04-12T20:18:13Z |
format | Article |
id | doaj.art-8329430b4eb744d98995ca2f38b7e690 |
institution | Directory Open Access Journal |
issn | 2196-1115 |
language | English |
last_indexed | 2024-04-12T20:18:13Z |
publishDate | 2017-07-01 |
publisher | SpringerOpen |
record_format | Article |
series | Journal of Big Data |
spelling | doaj.art-8329430b4eb744d98995ca2f38b7e6902022-12-22T03:18:03ZengSpringerOpenJournal of Big Data2196-11152017-07-014113010.1186/s40537-017-0086-3iiHadoop: an asynchronous distributed framework for incremental iterative computationsAfaf G. Bin Saadon0Hoda M. O. Mokhtar1Faculty of Computers and Information, Cairo UniversityFaculty of Computers and Information, Cairo UniversityAbstract It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new systems to perform large-scale data computations incrementally. MapReduce was recently introduced to provide an efficient approach for handling large-scale data computations. Nevertheless, it turned to be inefficient in supporting the processing of small incremental data. While many previous systems have extended MapReduce to perform iterative or incremental computations, these systems are still inefficient and too expensive to perform large-scale iterative computations on changing data. In this paper, we present a new system called iiHadoop, an extension of Hadoop framework, optimized for incremental iterative computations. iiHadoop accelerates program execution by performing the incremental computations on the small fraction of data that is affected by changes rather than the whole data. In addition, iiHadoop improves the performance by executing iterations asynchronously, and employing locality-aware scheduling for the map and reduce tasks taking into account the incremental and iterative behavior. An evaluation for the proposed iiHadoop framework is presented using examples of iterative algorithms, and the results showed significant performance improvements over comparable existing frameworks.http://link.springer.com/article/10.1186/s40537-017-0086-3Big dataDistributed systemsHadoop frameworkIterative processingIncremental computation |
spellingShingle | Afaf G. Bin Saadon Hoda M. O. Mokhtar iiHadoop: an asynchronous distributed framework for incremental iterative computations Journal of Big Data Big data Distributed systems Hadoop framework Iterative processing Incremental computation |
title | iiHadoop: an asynchronous distributed framework for incremental iterative computations |
title_full | iiHadoop: an asynchronous distributed framework for incremental iterative computations |
title_fullStr | iiHadoop: an asynchronous distributed framework for incremental iterative computations |
title_full_unstemmed | iiHadoop: an asynchronous distributed framework for incremental iterative computations |
title_short | iiHadoop: an asynchronous distributed framework for incremental iterative computations |
title_sort | iihadoop an asynchronous distributed framework for incremental iterative computations |
topic | Big data Distributed systems Hadoop framework Iterative processing Incremental computation |
url | http://link.springer.com/article/10.1186/s40537-017-0086-3 |
work_keys_str_mv | AT afafgbinsaadon iihadoopanasynchronousdistributedframeworkforincrementaliterativecomputations AT hodamomokhtar iihadoopanasynchronousdistributedframeworkforincrementaliterativecomputations |