Hadoop is a standard implementation of MapReduce framework for running data-intensive applications on the clusters of commodity servers. By thoroughly studying the framework we find out that the shuffle phase, all-to-all input data fetching phase in reduce task significantly affect the application performance. There is a problem of variance in both the intermediate key's frequencies and their distribution among data nodes throughout the cluster in Hadoop's MapReduce system. This variance in system causes network overhead which leads to unfairness on the reduce input among different data nodes in the cluster. Because of the above problem, applications experience performance degradation due to shuffle phase of MapReduce applications. ...
MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data l...
MapReduce has gradually become the framework of choice for ”big data”. The MapReduce model allows fo...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...
International audienceMapReduce is emerging as a prominent tool for big data processing. Data locali...
Abstract MapReduce is emerging as a prominent tool for big data processing. Data locality is a key f...
Over the last ten years MapReduce has emerged as one of the staples of distributed computing both in...
Data skew, cluster heterogeneity, and network traffic are three issues that significantly influence ...
MapReduce is an effective programming model for large-scale data-intensive computing applications. H...
The Hadoop framework has been developed to effectively process data-intensive MapReduce applications...
The healthcare industry has generated large amounts of data, and analyzing these has emerged as an i...
Hadoop has been developed to process the data-intensive applications. However, the current data-dist...
In the context of Hadoop, recent studies show that the shuffle operation accounts for as much as a t...
This research proposes a novel runtime system, Habanero Hadoop, to tackle the inefficient utilizatio...
The performance of MapReduce greatly depends on its data splitting process which happens before the ...
International audienceReducing data transfer in MapReduce's shuffle phase is very important because ...
MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data l...
MapReduce has gradually become the framework of choice for ”big data”. The MapReduce model allows fo...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...
International audienceMapReduce is emerging as a prominent tool for big data processing. Data locali...
Abstract MapReduce is emerging as a prominent tool for big data processing. Data locality is a key f...
Over the last ten years MapReduce has emerged as one of the staples of distributed computing both in...
Data skew, cluster heterogeneity, and network traffic are three issues that significantly influence ...
MapReduce is an effective programming model for large-scale data-intensive computing applications. H...
The Hadoop framework has been developed to effectively process data-intensive MapReduce applications...
The healthcare industry has generated large amounts of data, and analyzing these has emerged as an i...
Hadoop has been developed to process the data-intensive applications. However, the current data-dist...
In the context of Hadoop, recent studies show that the shuffle operation accounts for as much as a t...
This research proposes a novel runtime system, Habanero Hadoop, to tackle the inefficient utilizatio...
The performance of MapReduce greatly depends on its data splitting process which happens before the ...
International audienceReducing data transfer in MapReduce's shuffle phase is very important because ...
MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data l...
MapReduce has gradually become the framework of choice for ”big data”. The MapReduce model allows fo...
Algorithms for mitigating imbalance of the MapReduce computa-tions are considered in this paper. Map...