305,848 research outputs found
ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads
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 files, web pages and 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 the energy, and has an Energy Delay Product (EDP) that
is 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
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Performance Analysis of Hadoop MapReduce And Apache Spark for Big Data
In the recent era, information has evolved at an exponential rate. In order to obtain new insights, this information must be carefully interpreted and analyzed. There is, therefore, a need for a system that can process data efficiently all the time. Distributed cloud computing data processing platforms are important tools for data analytics on a large scale. In this area, Apache Hadoop (High-Availability Distributed Object-Oriented Platform) MapReduce has evolved as the standard. The MapReduce job reads, processes its input data and then returns it to Hadoop Distributed Files Systems (HDFS). Although there is limitation to its programming interface, this has led to the development of modern data flow-oriented frameworks known as Apache Spark, which uses Resilient Distributed Datasets (RDDs) to execute data structures in memory. Since RDDs can be stored in the memory, algorithms can iterate very efficiently over its data many times.
Cluster computing is a major investment for any organization that chooses to perform Big Data Analysis. The MapReduce and Spark were indeed two famous open-source cluster-computing frameworks for big data analysis. Cluster computing hides the task complexity and low latency with simple user-friendly programming. It improves performance throughput, and backup uptime should the main system fail. Its features include flexibility, task scheduling, higher availability, and faster processing speed. Big Data analytics has become more computer-intensive as data management becomes a big issue for scientific computation. High-Performance Computing is undoubtedly of great importance for big data processing. The main application of this research work is towards the realization of High-Performance Computing (HPC) for Big Data Analysis.
This thesis work investigates the processing capability and efficiency of Hadoop MapReduce and Apache Spark using Cloudera Manager (CM). The Cloudera Manager provides end-to-end cluster management for Cloudera Distribution for Apache Hadoop (CDH). The implementation was carried out with Amazon Web Services (AWS). Amazon Web Service is used to configure window Virtual Machine (VM). Four Linux In-stances of free tier eligible t2.micro were launched using Amazon Elastic Compute Cloud (EC2). The Linux Instances were configured into four cluster nodes using Secure Socket Shell (SSH).
A Big Data application is generated and injected while both MapReduce and Spark job are run with different queries such as scan, aggregation, two way and three-way join. The time taken for each task to be completed are recorded, observed, and thoroughly analyzed. It was observed that Spark executes job faster than MapReduce
RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor
Heterogeneous omics data, increasingly collected through high-throughput technologies, can contain hidden answers to very important and still unsolved biomedical questions. Their integration and processing are crucial mostly for tertiary analysis of Next Generation Sequencing data, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures
Multi-node approach for map data processing
OpenStreetMap (OSM) is a popular collaborative open-source project that offers free editable map across the whole world. However, this data often needs a further on-purpose processing to become the utmost valuable information to work with. That is why the main motivation of this paper is to propose a design for big data processing along with data mining leading to the obtaining of statistics with a focus on the detail of a traffic data as a result in order to create graphs representing a road network. To ensure our High-Performance Computing (HPC) platform routing algorithms work correctly, it is absolutely essential to prepare OSM data to be useful and applicable for above-mentioned graph, and to store this persistent data in both spatial database and HDF5 format.Web of Science8971049
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