1,485 research outputs found
The state of SQL-on-Hadoop in the cloud
Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud,
and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark.
The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines.
The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization.
The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some
providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under
the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat
de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
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
Hyper Converged Infrastructures: Beyond virtualization
Hyper Convergence has brought virtualization and IT strategies to a new
level. Datacenters are undergoing a deep paradigm shift from a hardware-centric
to an application-centric approach which leverages on software defined
architectures, while IT is more and more being delivered as services rather
than assets or products. Throughout different evolving phases since the initial
attempts to convergence, the concept has been refined down to a level
where,ultimately, a whole datacenter could be fully managed from a centralized
single point, abstracting the whole hardware layer and exposing it to the
administrators as a transparent pool of resources. This paper analyzes the
evolution of infrastructures and tries to dig into the reality and convenience
of Hyper Convergence
Proceedings of the NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications
The proceedings of the National Space Science Data Center Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications held July 23 through 25, 1991 at the NASA/Goddard Space Flight Center are presented. The program includes a keynote address, invited technical papers, and selected technical presentations to provide a broad forum for the discussion of a number of important issues in the field of mass storage systems. Topics include magnetic disk and tape technologies, optical disk and tape, software storage and file management systems, and experiences with the use of a large, distributed storage system. The technical presentations describe integrated mass storage systems that are expected to be available commercially. Also included is a series of presentations from Federal Government organizations and research institutions covering their mass storage requirements for the 1990's
D-SPACE4Cloud: A Design Tool for Big Data Applications
The last years have seen a steep rise in data generation worldwide, with the
development and widespread adoption of several software projects targeting the
Big Data paradigm. Many companies currently engage in Big Data analytics as
part of their core business activities, nonetheless there are no tools and
techniques to support the design of the underlying hardware configuration
backing such systems. In particular, the focus in this report is set on Cloud
deployed clusters, which represent a cost-effective alternative to on premises
installations. We propose a novel tool implementing a battery of optimization
and prediction techniques integrated so as to efficiently assess several
alternative resource configurations, in order to determine the minimum cost
cluster deployment satisfying QoS constraints. Further, the experimental
campaign conducted on real systems shows the validity and relevance of the
proposed method
Letter from the Special Issue Editor
Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies
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