758 research outputs found

    Hadoop Based Data Intensive Computation on IAAS Cloud Platforms

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    Cloud computing is a relatively new form of computing which uses virtualized resources. It is dynamically scalable and is often provided as pay for use service over the Internet or Intranet or both. With increasing demand for data storage in the cloud, the study of data-intensive applications is becoming a primary focus. Data intensive applications are those which involve high CPU usage, processing large volumes of data typically in size of hundreds of gigabytes, terabytes or petabytes. The research in this thesis is focused on the Amazon’s Elastic Cloud Compute (EC2) and Amazon Elastic Map Reduce (EMR) using HiBench Hadoop Benchmark suite. HiBench is a Hadoop benchmark suite and is used for performing and evaluating Hadoop based data intensive computation on both these cloud platforms. Both quantitative and qualitative comparisons of Amazon EC2 and Amazon EMR are presented. Also presented are their pricing models and suggestions for future research

    ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads

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    ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to 20GB20GB files, 5M5M web pages and 500M500M tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes 13rd\frac{1}{3}^{rd} the energy, and has an Energy Delay Product (EDP) that is 5071%50-71\% lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.Comment: Accepted for publication in the Proceedings of the 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 201

    Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud

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    [Abstract] The advent of cloud computing technologies, which dynamically provide on-demand access to computational resources over the Internet, is offering new possibilities to many scientists and researchers. Nowadays, Infrastructure as a Service (IaaS) cloud providers can offset the increasing processing requirements of data-intensive computing applications, becoming an emerging alternative to traditional servers and clusters. In this paper, a comprehensive study of the leading public IaaS cloud platform, Amazon EC2, has been conducted in order to assess its suitability for data-intensive computing. One of the key contributions of this work is the analysis of the storage-optimized family of EC2 instances. Furthermore, this study presents a detailed analysis of both performance and cost metrics. More specifically, multiple experiments have been carried out to analyze the full I/O software stack, ranging from the low-level storage devices and cluster file systems up to real-world applications using representative data-intensive parallel codes and MapReduce-based workloads. The analysis of the experimental results has shown that data-intensive applications can benefit from tailored EC2-based virtual clusters, enabling users to obtain the highest performance and cost-effectiveness in the cloud.Ministerio de Economía y Competitividad; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/055Ministerio de Educación y Ciencia; AP2010-434
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