Google’s MapReduce programming model serves for processing and generating large data sets in a massively parallel manner (subject to a suitable implementation of the model). We deliver the first rigorous description of the model. To this end, we reverse-engineer the seminal MapReduce paper and we capture our observations, assumptions and recommendations as an executable specification. We also i...

MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model bey...

This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups v...

MapReduce is a programming model for data-parallel programs originally intended for data centers. MapReduce simplifies parallel programming, hiding synchronization and task management. These properties make it a promising programming model for future processors with many cores, and existing MapReduce libraries such as Phoenix have demonstrated that applications written with MapReduce perform co...

Energy efficiency has emerged as a crucial optimization goal in data centers. MapReduce has become a popular and even fashionable distributed processing model for parallel computing in data centers. Hadoop is an open-source implementation of MapReduce, which is widely used for short jobs requiring low response time. In this paper, we conduct an indepth study of the energy efficiency for MapRedu...

Simulated annealing’s high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used ...

Nowadays, the volume of data is growing at an nprecedented rate, big data mining , and knowledge discovery have become a new challenge in the era of data mining and machine learning. Rough set theory for knowledge acquisition has been successfully applied in data mining. The MapReduce technique, received more attention from scientific community as well as industry for its applicability in big d...

This paper describes preliminary work in developing a modeldriven approach to conducting price/performance tradeo s for Cloudbased MapReduce application deployment. The need for this work stems from the signi cant variability in both the MapReduce application characteristics and price/performance characteristics of the underlying cloud platform. Our approach involves a model-based machine learn...

MapReduce is an emerging and widely used programming model for large-scale data parallel applications that require to process large amount of raw data. There are several implementations of MapReduce framework, among which Apache Hadoop is the most commonly used and open source implementaion. These frameworks are rarely deployed on supercomputers as massive as Blue Waters. We want to evaluate ho...

The MapReduce programming model has become widely adopted for large scale analytics on big data. MapReduce systems such as Hadoop have many tuning parameters, many of which have a significant impact on performance. The map and reduce functions that make up a MapReduce job are developed using arbitrary programming constructs, which make them black-box in nature and therefore renders it difficult...