Jorge Veiga

University of A Coruña | UDC · Department of Electronics and Systems

Java has been the backbone of Big Data processing for more than a decade due to its interesting features such as object orientation, cross-platform portability and good programming productivity. In fact, most popular Big Data frameworks such as Hadoop and Spark are implemented in Java or using other languages designed to run on the Java Virtual Mac...

Apache Hadoop is a widely used MapReduce framework for storing and processing large amounts of data. However, it presents some performance issues that hinder its utilization in many practical use cases. Although existing alternatives like Spark or Hama can outperform Hadoop, they require to rewrite the source code of the applications due to API inc...

As the memory capacity of computational systems increases, the in-memory data management of Big Data processing frameworks becomes more crucial for performance. This paper analyzes and improves the memory efficiency of Flame-MR, a framework that accelerates Hadoop applications, providing valuable insight into the impact of memory management on perf...

As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metric...

This paper presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single-end and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-plat...

Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) atte...

The increasing adoption of Big Data analytics has led to a high demand for efficient technologies in order to manage and process large datasets. Popular MapReduce frameworks such as Hadoop are being replaced by emerging ones like Spark or Flink, which improve both the programming APIs and performance. However, few works have focused on comparing th...

The popularity of Big Data computing models like MapReduce has caused the emergence of many frameworks oriented to High Performance Computing (HPC) systems. The suitability of each one to a particular use case depends on its design and implementation, the underlying system resources and the type of application to be run. Therefore, the appropriate...

The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available...