Hadoop MapReduce framework is an important distributed processing model for large-scale data intensive applications. The current Hadoop and the existing Hadoop distributed file system’s rack-aware data placement strategy in MapReduce in the homogeneous Hadoop cluster assume that each node in a cluster has the same computing capacity and a same workload is assigned to each node. Default Hadoop d...

MapReduce is an enabling technology in support of Cloud Computing. Hadoop which is a mapReduce implementation has been widely used in developing MapReduce applications. This paper presents Hadoop simulatorHaSim, MapReduce simulator which builds on top of Hadoop. HaSim models large number of parameters that can affect the behaviors of MapReduce nodes, and thus it can be used to tune the performa...

MapReduce has been prevalent for running data-parallel applications. By hiding other non-functionality parts such as parallelism, fault tolerance and load balance from programmers, MapReduce significantly simplifies the programming of large clusters. Due to the mentioned features of MapReduce above, researchers have also explored the use of MapReduce on other application domains, such as machin...

The MapReduce programming model, proposed by Google, offers a simple and efficient way to perform distributed computation over large data sets. The Apache Hadoop framework is a free and open-source implementation of MapReduce. To simplify the usage of Hadoop, Amazon Web Services provides Elastic MapReduce, a web service that enables users to submit MapReduce jobs. Elastic MapReduce takes care o...

MapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have gre...

This paper addresses the problem of skyline computation under the MapReduce framework. As a parallel programming model for data-intensive computing applications, MapReduce runs on a cluster of commercial PCs with the main idea of task decomposition and result reduction. Based on different data partitioning strategies, three MapReduce style skyline computation algorithms are developed: MapReduce...

MapReduce is a popular programming model for distributed data processing and Big Data applications. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on-demand fault-tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates a novel solution tha...

The MapReduce programming model, introduced by Google, offers a simple and efficient way of performing distributed computation over large data sets. Although Google’s implementation is proprietary, MapReduce can be leveraged by anyone using the free and open source Apache Hadoop framework. To simplify the usage of Hadoop in the cloud, Amazon Web Services offers Elastic MapReduce, a web service ...