Handling skew is one of the major challenges in query processing. In distributed computational environments such as MapReduce, uneven distribution of the data to the servers is not desired. One of the dominant measures that we want to optimize in distributed environments is communication cost. In a MapReduce job this is the amount of data that is transferred from the mappers to the reducers. In...

Access plan recommendation is a query optimization approach that executes new queries using prior created execution plans (QEPs). The optimizer divides the space into clusters in mentioned method. However, traditional clustering algorithms take significant amount of time for such large datasets. MapReduce distributed computing model provides efficient solutions storing and processing vast quant...

As data are the basis of information systems, using Hadoop to rapidly extract useful information from massive data of an enterprise has become an efficient method for programmers in the process of application development. This chapter introduces the MapReduce framework, an excellent distributed and parallel computing model. For the increasing data and cluster scales, to avoid scheduling delays,...

THIS CHAPTER DESCRIBES THE DESIGN AND IMPLEMENTATION OF MAPREDUCE, a programming system for large-scale data processing problems. MapReduce was developed as a way of simplifying the development of large-scale computations at Google. MapReduce programs are automatically parallelized and executed on a large cluster of commodity machines. The runtime system takes care of the details of partitionin...

Several novel data center networking (DCN) topologies have been proposed to improve the topological properties of data centers. Unfortunately, it is ignored that whether these topologies are suited for the online applications and infrastructure services running on the corresponding data centers. In this paper, we propose a novel DCN topology, named HyperFat-tree Network (HFN). HFN incarnates th...

MapReduce is implementation for generating large data sets with a parallel, distributed algorithm on a cluster. Hadoop is open source implementation of the MapReduce programming datamodel used for large-scale parallel applications such as web indexing, data mining, and scientific simulation. Hadoop-A framework is able to levitate Hadoop acceleration and give significant performance compared to ...

Recently, MapReduce has been a key and popular technology for tackling data-intensive applications. But its two master servers in current MapReduce implementations have a single-point-of-failure problem, which may interrupt MapReduce operations and filesystem services. In this paper, we propose a hybrid takeover scheme, called PAReS (Proactive and Adaptive Redundant System). To further improve ...

BACKGROUND Explosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/...

MapReduce distributed data processing architecture has become the de-facto data-intensive analysis mechanism in compute clouds and in commodity clusters, mainly due to its excellent fault tolerance features, scalability, ease of use and the simpler programming model. MapReduceRoles for Azure (MR4Azure) is a decentralized, dynamically scalable MapReduce runtime we developed for Windows Azure Clo...

Map Reduce has gained remarkable significance as a prominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytics where massive data analysis is required, but still it is constantly being explored on different parameters such as perf...