MapReduce is a programming model and an associated implementation for processing parallel data, which is widely used in Cloud computing environments. However, the traditional MapReduce system is based on a centralized master-slave structure. While, along with the increase of the number of MapReduce jobs submitted and system scale, the master node will become the bottleneck of the system. To imp...

Supporting real-time jobs on MapReduce systems is particularly challenging due to the heterogeneity of the environment, the load imbalance caused by skewed data blocks, as well as real-time response demands imposed by the applications. In this paper we describe our approach for scheduling real-time, skewed MapReduce jobs in heterogeneous systems. Our approach comprises the following components:...

MapReduce is a popular parallel computing paradigm for Big Data processing in clusters and data centers. It is observed that different job execution orders and MapReduce slot configurations for a MapReduce workload have significantly different performance with regarding to the makespan, total completion time, system utilization and other performance metrics. There are quite a few algorithms on ...

Undoubtedly, the MapReduce is the most powerful programming paradigm in distributed computing. The enhancement of the MapReduce is essential and it can lead the computing faster. Therefore, there are many scheduling algorithms to discuss based on their characteristics. Moreover, there are many shortcoming to discover in this field. In this article, we present the state-of-the-art scheduling alg...

Today, MapReduce and virtual cluster are sharp swords for this big data and cloud computing era. To combine these two emerging technologies, it brings feasible-scalability, easy-management, fast-deployment and high-efficiency with the system. As every sword has two sides, the I/O bottleneck of virtualization technologies may seriously impacts on the performance of MapReduce cluster which deals ...

Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data amongst a large number of computers (nodes...

This paper proposes an Hadoop library, named M3, for performing dense and sparse matrix multiplication in MapReduce. The library features multi-round MapReduce algorithms that allow to tradeoff round number with the amount of data shuffled in each round and the amount of memory required by reduce functions. We claim that multi-round MapReduce algorithms are preferable in cloud settings to tradi...

The eXtensible Stylesheet Language Transformation (XSLT) is a de-facto standard for XML data transforming and extracting. Efficient processing of large amounts of XML data brings challenges to conventional XSLT processors, which are designed to run in a single machine context. To solve these data-intensive problems, MapReduce paradigm in the cloud computing domain has received a comprehensive a...

Analyzing patterns in large-scale graphs, such as social networks (e.g. Facebook, Linkedin, Twitter) has many applications including community identification, blog analysis, intrusion and spamming detections. Currently, it is impossible to process information in large-scale graphs with millions even billions of edges with a single computer. In this paper, we take advantage of MapReduce, a progr...

Traveling Salesman Problem (TSP) is one of the most common studied problems in combinatorial optimization. Given the list of cities and distances between them, the problem is to find the shortest tour possible which visits all the cities in list exactly once and ends in the city where it starts. Despite the Traveling Salesman Problem is NP-Hard, a lot of methods and solutions are proposed to th...