scholarly journals Open Source Cloud Computing: An Experience Case of Geo-based Image Handling in Amazon Web Services

2012 ◽  
Vol 28 (3) ◽  
pp. 337-346 ◽  
Author(s):  
Ki-Won Lee
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Open Source ◽  
Amazon Web Services

scholarly journals IP Telephony Applicability in Cloud Computing

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 128-133
Author(s):  
Francisco Palacios ◽  
Mitchell Vásquez Bermúdez ◽  
Fausto Orozco ◽  
Diana Espinoza Villón
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Open Source ◽  
Open Source Software ◽  
Ip Telephony ◽  
Amazon Web Services ◽  
Private Branch Exchange ◽  
And Control

This paper carries out a research related to the applicability of VoIP over Cloud Computing to guarantee service stability and elasticity of the organizations. In this paper, Elastix is used as an open source software that allows the management and control of a Private Branch Exchange (PBX); and for developing, it is used the services given Amazon Web Services due to their leadership and experience in cloud computing providing security, scalability, backup service and feasibility for the users.

scholarly journals Biomedical Cloud Computing With Amazon Web Services

2011 ◽  
Vol 7 (8) ◽  
pp. e1002147 ◽  
Author(s):  
Vincent A. Fusaro ◽  
Prasad Patil ◽  
Erik Gafni ◽  
Dennis P. Wall ◽  
Peter J. Tonellato
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Amazon Web Services

scholarly journals An Investigation of Microsoft Azure and Amazon Web Services from Users’ Perspectives

2019 ◽  
Vol 14 (10) ◽  
pp. 217 ◽  
Author(s):  
Rizik M. H. Al-Sayyed ◽  
Wadi’ A. Hijawi ◽  
Anwar M. Bashiti ◽  
Ibrahim AlJarah ◽  
Nadim Obeid ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Service Level Agreement ◽  
Service Level ◽  
Point Of View ◽  
Survey Study ◽  
Utility Computing ◽  
User Survey ◽  
Amazon Web Services ◽  
Microsoft Azure

Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users’ perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users’ perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors.

CONDE: Climate simulation ON DEmand using HPCaaS

2020 ◽  
Author(s):  
Diego A. Pérez Montes ◽  
Juan A. Añel ◽  
Javier Rodeiro
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Climate Simulation ◽  
On Demand ◽  
Ensemble Simulations ◽  
Amazon Web Services ◽  
Computing Environments ◽  
Microsoft Azure

<p><strong>CONDE (Climate simulation ON DEmand)</strong> is the final result of our work and research about climate and meteorological simulations over an HPC as a Service (HPCaaS) model. On our architecture we run very large climate ensemble simulations using a, adapted, WRF version that is executed on-demand and that can be deployed over different Cloud Computing environments (like Amazon Web Services, Microsoft Azure or Google Cloud) and that uses BOINC as middleware for the tasks execution and results gathering. Here, we also present as well some basic examples of applications and experiments to verify that the simulations ran in our system are correct and show valid results. </p>

Serverless Computing Using Amazon Web Services

2020 ◽  
Vol 17 (8) ◽  
pp. 3581-3585
Author(s):  
M. S. Roobini ◽  
Selvasurya Sampathkumar ◽  
Shaik Khadar Basha ◽  
Anitha Ponraj
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Software Design ◽  
Web Application ◽  
Cloud Provider ◽  
Business Logic ◽  
Compute Model ◽  
Amazon Web Services ◽  
The Way

In the last decade cloud computing transformed the way in which we build applications. The boom in cloud computing helped to develop new software design and architecture. Helping the developers to focus more on the business logic than the infrastructure. FaaS (function as a service) compute model it gave developers to concentrate only on the application code and rest of the factors will be taken care by the cloud provider. Here we present a serverless architecture of a web application built using AWS services and provide detail analysis of lambda function and micro service software design implemented using these AWS services.

scholarly journals Long Paddock: climate risk and grazing information for Australian rangelands and grazing communities

2019 ◽  
Vol 41 (3) ◽  
pp. 225 ◽  
Author(s):  
G. Stone ◽  
R. Dalla Pozza ◽  
J. Carter ◽  
G. McKeon
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Natural Resource ◽  
Climate Risk ◽  
Resource Base ◽  
Computing Platform ◽  
Amazon Web Services ◽  
Cloud Computing Platform ◽  
Natural Resource Base ◽  
Tailored Information

The Queensland Government’s Long Paddock website has been redeveloped on Amazon Web Services cloud computing platform, to provide Australian rangelands and grazing communities (i.e. rural landholders, managers, pastoralists (graziers), researchers, advisors, students, consultants and extension providers) with easier access to seasonal climate and pasture condition information. The website provides free, tailored information and services to support management decisions to maximise productivity, while maintaining the natural resource base. For example, historical rainfall and pasture analyses (i.e. maps, posters and data) have been developed to assist in communicating the risk of multi-year droughts that are a feature of Queensland’s highly variable climate.

scholarly journals Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

2015 ◽  
Author(s):  
Abhinav Nellore ◽  
Leonardo Collado-Torres ◽  
Andrew E Jaffe ◽  
José Alquicira-Hernández ◽  
Jacob Pritt ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Web Services ◽  
Open Source ◽  
Rna Sequencing ◽  
Open Source Software ◽  
Rna Seq ◽  
Spliced Alignment ◽  
Amazon Web Services ◽  
Scalable Analysis ◽  
Multiple Samples

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

scholarly journals Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge

2019 ◽  
Author(s):  
David Liu ◽  
Matthew Salganik
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Fragile Families ◽  
Special Issue ◽  
Computing Environment ◽  
Raw Data ◽  
Academic Researchers ◽  
Amazon Web Services ◽  
Software Engineers ◽  
Published Paper

Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results in a published paper using the original author's raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this paper, we describe our approach to enabling computational reproducibility for the 12 papers in this special issue of Socius about the Fragile Families Challenge. Our approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools enabled us to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on our successes and struggles, we conclude with recommendations to authors and journals.

scholarly journals MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

2018 ◽  
Author(s):  
Li Chen ◽  
Bai Zhang ◽  
Michael Schnaubelt ◽  
Punit Shah ◽  
Paul Aiyetan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Open Source ◽  
High Performance ◽  
Large Scale ◽  
Rapid Development ◽  
Data File ◽  
Data Sets ◽  
Proteomics Data ◽  
Amazon Web Services

ABSTRACTRapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and high-performance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/

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