Many analytic applications built on Hadoop ecosystem have a propensity to iteratively perform repetitive operations on same input data. To remove the burden of these repetitive operations, new frameworks for MapReduce have been introduced, which make users follow its programming model. We propose a solution to the problem of application rewriting that newer frameworks impose. We re-architected Hadoop core to add in-memory caching and cache-aware task-scheduling.We set out to match the performance of a state-of-the-art high speed, in-memory MapReduce architecture with caching (Spark). While Spark reimplements the MapReduce paradigm, it comes with a new set of new API's and abstractions. We maintain the familiar Hadoop framework and API's, th...
Part 2: Parallel and Multi-Core TechnologiesInternational audienceAs a widely used programming model...
Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. Th...
Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. T...
Many analytic applications built on Hadoop ecosystem have a propensity to iteratively perform repeti...
In this paper, we investigate techniques to effectively orchestrate HDFS in-memory caching for Hadoo...
The demand for highly parallel data processing platform was growing due to an explosion in the numbe...
The Big-data refers to the huge scale distributed data processing applications that operate on unusu...
Abstract The buzz-word big-data refers to the large-scale distributed data processing applications t...
Data is being generated at an enormous rate, due to online activities and use of resources related t...
Abstract: The buzz-word big-data refers to the large-scale distributed data processing applications ...
International audienceApache Hadoop is a widely used MapReduce framework for storing and processing ...
Abstract—The MapReduce platform has been widely used for large-scale data processing and analysis re...
MapReduce is a computing paradigm that has gained a lot of at-tention in recent years from industry ...
International audienceNowadyas, we are witnessing the fast production of very large amount of data, ...
This research proposes a novel runtime system, Habanero Hadoop, to tackle the inefficient utilizatio...
Part 2: Parallel and Multi-Core TechnologiesInternational audienceAs a widely used programming model...
Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. Th...
Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. T...
Many analytic applications built on Hadoop ecosystem have a propensity to iteratively perform repeti...
In this paper, we investigate techniques to effectively orchestrate HDFS in-memory caching for Hadoo...
The demand for highly parallel data processing platform was growing due to an explosion in the numbe...
The Big-data refers to the huge scale distributed data processing applications that operate on unusu...
Abstract The buzz-word big-data refers to the large-scale distributed data processing applications t...
Data is being generated at an enormous rate, due to online activities and use of resources related t...
Abstract: The buzz-word big-data refers to the large-scale distributed data processing applications ...
International audienceApache Hadoop is a widely used MapReduce framework for storing and processing ...
Abstract—The MapReduce platform has been widely used for large-scale data processing and analysis re...
MapReduce is a computing paradigm that has gained a lot of at-tention in recent years from industry ...
International audienceNowadyas, we are witnessing the fast production of very large amount of data, ...
This research proposes a novel runtime system, Habanero Hadoop, to tackle the inefficient utilizatio...
Part 2: Parallel and Multi-Core TechnologiesInternational audienceAs a widely used programming model...
Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. Th...
Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. T...