619 research outputs found
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
Analysis and evaluation of MapReduce solutions on an HPC cluster
This is a post-peer-review, pre-copyedit version of an article published in Computers & Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1016/j.compeleceng.2015.11.021[Abstract] 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 resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications.Ministerio de Economía y Competitividad; TIN2013-42148-
Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures
One of the significant shifts of the next-generation computing technologies will certainly be in
the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD
landmark, evolved as a widely deployed BD operating system. Its new features include
federation structure and many associated frameworks, which provide Hadoop 3.x with the
maturity to serve different markets. This dissertation addresses two leading issues involved in
exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely,
(i)Scalability that directly affects the system performance and overall throughput using
portable Docker containers. (ii) Security that spread the adoption of data protection practices
among practitioners using access controls. An Enhanced Mapreduce Environment (EME),
OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker
(BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for
data streaming to the cloud computing are the main contribution of this thesis study
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