MapReduce is a widely used framework for massive data processing. It was originally designed to overcome the I/O bottleneck, and enabled us to process Bigdata with the commodity clusters systems. However, several existing work have recently shown that the emerging high speed storage and network devices are capable to remove the I/O bottleneck and made the CPU the next serious bottleneck in the ...

In this paper, we describe efficient MapReduce simulations of parallel algorithms specified in the BSP and PRAM models. We also provide some applications of these simulation results to problems in parallel computational geometry for the MapReduce framework, which result in efficient MapReduce algorithms for sorting, 1-dimensional all nearest-neighbors, 2-dimensional convex hulls, 3-dimensional ...

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...

Tag affinity is the relationship between tags. It is a useful information for search and recommendation in social tagging systems. Tag affinity is measured by several types of tag cooccurrence frequency. The computation of tag affinity is a time-consuming task as the tagging information is accumulated. To alleviate this problem, we propose a parallel tag affinity computation method using MapRed...

MapReduce has become a widely used computing model for largescale data processing in clusters and data centers. A MapReduce workload generally contains multiple jobs. Due to the general execution constraints that map tasks are executed before reduce tasks, different job execution orders in a MapReduce workload can have significantly different performance and system utilization. This paper propo...

Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for incomplete data. Applying the EM algorithm to learn BN parameters is unfortunately susceptible to local optima and prone to premature convergence. We develop and experiment with two methods for improving EM parameter learning by using MapReduce: Age-Layered Expectation Maximization (ALEM) a...

Several novel data center network structures have been proposed to improve the topological properties of data centers. A common characteristic of these structures is that they are designed for supporting general applications and services. Consequently, these structures do not match well with the specific requirements of some dedicated applications. In this paper, we propose a hyper-fat-tree net...

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets limited because computational complexity hierarchical methods has quadratic lower bound number objects be clustered. MapReduce popular programming model speed up mining machine lea...