Microsoft Windows Azure Research Articles

WITHDRAWN: Cognitive analysis of deploying web applications on microsoft windows azure and amazon web services in global scenario

Abstract In the global technological era, Amazon Web Service and Microsoft Azure Cloud technology are widely used as public cloud all around world for large data systems. AWS and AZURE cloud server provide public, private, hybrid and social services to various businesses and other large organizations. Both AWS and AZURE can be upgraded to prevent data imposition with various safekeeping features where many users share the same cloud of data for storage and retrieval purpose. Analytical comparisons among various features such as platform autonomy, mass employment, customer needs, security, portability, data size, and other related resources are discussed. This paper provides a cognitive analysis of AWS and AZURE highlighting various options offering for deploying web applications by both providers.

Optical Character Recognition as a Cloud Service in Azure Architecture

technology has become an extremely attractive area of research over the last few years. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation and complex background of image. There is need to convert paper books and documents into text. OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. A cloud based Optical Character Recognition Technology was used. This was powered on Microsoft Windows Azure in form of a Web Application Programming Interface that load images to an Optical Character Recognition server, process with necessary recognition, export parameters, and obtain the results of the processing. The key idea is to bring together the advantages of the Optical Character Recognition technology and cloud computing in one place in other to enable quicker access and faster turn out time, processing period, and increasing efficiency across the board for application users. The methodology adopted is the object oriented methodology. This was achieved using JAVA programming language.

Microsoft Windows Azure: Developing Applications for Highly Available Storage of Cloud Service

Microsoft Windows Azure: Developing Applications for Highly Available Storage of Cloud Service

Reactive transport modeling of thorium in a cloud computing environment

Despite the long standing interest in modeling the fate and environmental impacts of radionuclides, simulations addressing the fate and transport of rare earth elements (REEs) and thorium (Th) have received comparably little attention. This study presents an architecture that enables reactive transport modeling and parameter sensitivity analysis on cloud computing platforms. We adapted an existing groundwater modeling framework to perform some of the computationally most expensive steps within a cloud environment based on Microsoft Windows Azure. The cloud computing architecture was evaluated and validated through the development of a schematic, cross sectional model along a transect across a tailings impoundment at a REE mine tailings site in northwest China. The model framework employs a suite of flow, solute transport and reactive transport simulation tools, i.e., MODFLOW, MT3DMS, and PHT3D. On the basis of our model simulations, the collection-trench for the impoundment constructed above the ground surface appears to collect a substantial portion of the leachate fluxes, but the remainder will bypass the trench and migrate downstream. Those bypassed leachate fluxes will subsequently interact with downstream fluviolacustrine aquifers and eventually discharge into the Yellow River south of the study site under the idealized simulation environment. Further investigations of the hydraulic parameters of the aquifer system and the impoundment dam, and other geochemical characteristics are needed to elucidate the fate and transport of thorium and improve the reliability of the numerical model. Although the discussion and analysis of this study is tailored to thorium reactive transport modeling of a REE tailings impoundment, such a framework can also be applied to deploy different types of scientific modeling applications on Azure Cloud.

Achieving Message Board Function Based on Storage Services of the Windows Azure Platform

in order to study the development of the Windows Azure platform, the paper through the use of cloud computing, one of the platforms Windows Azure, using its Table Storage storage services,to realize a message board function; and realize a good combination of NET Framework and Windows Azure, and explore the Difference of the Microsoft Windows Azure cloud computing platform development and the difference between ordinary ASP.NET development.

Atmospheric monitoring network system based on cloud computing

In order to solve the problems of high hardware cost and information isolated island in the constructing and operating of atmospheric monitoring network system,this paper proposed an atmospheric monitoring network system based on Microsoft's Windows Azure cloud computing platform.This system provided access for automatic weather stations by General Packet Radio Service(GPRS) network technology,adopted Microsoft's Blob,Table storage and SQL Azure database to store massive data,developed and established Web portal on Windows Azure platform with ASP.NET and Flash technology,realized a unified access platform for users.The results demonstrate that this system can effectively solve the problem and create constructive value for establishing atmospheric monitoring network system in various ranges.

Developing cloud applications using the e-Science Central platform

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.

Geoprocessing in Cloud Computing platforms – a comparative analysis

The emergence of Cloud Computing technologies brings a new information infrastructure to users. Providing geoprocessing functions in Cloud Computing platforms can bring scalable, on-demand, and cost–effective geoprocessing services to geospatial users. This paper provides a comparative analysis of geoprocessing in Cloud Computing platforms – Microsoft Windows Azure and Google App Engine. The analysis compares differences in the data storage, architecture model, and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms; emphasizes the importance of virtualization; recommends applications of hybrid geoprocessing Clouds, and suggests an interoperable solution on geoprocessing Cloud services. The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern, once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies. The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms. The tested services are developed using geoprocessing algorithms from different vendors, GeoSurf and Java Topology Suite. The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.

Beyond the Hype: Cloud Computing in Analytics

Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection e fforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this 'data avalanche' becoming evermore overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade off between computational efficiency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches. We set out to discern one of these new hypes in analytics, cloud computing, and present a case study hereof which was performed at KU Leuven. In this study, we set up a benchmarking experiment using the Microsoft Windows Azure cloud platform with Techila Technologies middleware, and compare the results with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in the cloud.

Migration to Windows Azure – Analysis and Comparison

Cloud computing turned utility computing into a reality. Organizations may currently pay only for what they use, in computing power or storage capacity, as well as in electricity or other utilities. This enables a reduction of the investment in IT and leads to the more efficient use of datacenters, while allowing organizations to focus on the IT services they need to hire. To be able to fully benefit from the advantages of the cloud computing, organizations need to migrate their current applications to the cloud. After a brief introduction to cloud computing and to the Microsoft Windows Azure platform, this paper analyzes the migration of an on-premises web application, used on a secondary vocational school, to the cloud. It then compares the application's performance when deployed to a traditional Windows server versus its deployment to Windows Azure.

Classifying Proteins into Functional Groups Based on All-versus-All BLAST of 10 Million Proteins

To address the monumental challenge of assigning function to millions of sequenced proteins, we completed the first of a kind all-versus-all sequence alignments using BLAST for 9.9 million proteins in the UniRef100 database. Microsoft Windows Azure produced over 3 billion filtered records in 6 days using 475 eight-core virtual machines. Protein classification into functional groups was then performed using Hive and custom jars implemented on top of Apache Hadoop utilizing the MapReduce paradigm. First, using the Clusters of Orthologous Genes (COG) database, a length normalized bit score (LNBS) was determined to be the best similarity measure for classification of proteins. LNBS achieved sensitivity and specificity of 98% each. Second, out of 5.1 million bacterial proteins, about two-thirds were assigned to significantly extended COG groups, encompassing 30 times more assigned proteins. Third, the remaining proteins were classified into protein functional groups using an innovative implementation of a single-linkage algorithm on an in-house Hadoop compute cluster. This implementation significantly reduces the run time for nonindexed queries and optimizes efficient clustering on a large scale. The performance was also verified on Amazon Elastic MapReduce. This clustering assigned nearly 2 million proteins to approximately half a million different functional groups. A similar approach was applied to classify 2.8 million eukaryotic sequences resulting in over 1 million proteins being assign to existing KOG groups and the remainder clustered into 100,000 functional groups.

Cloud computing paradigms for pleasingly parallel biomedical applications

SUMMARYCloud computing offers exciting new approaches for scientific computing that leverage major commercial players’ hardware and software investments in large‐scale data centers. Loosely coupled problems are very important in many scientific fields, and with the ongoing move towards data‐intensive computing, they are on the rise. There exist several different approaches to leveraging clouds and cloud‐oriented data processing frameworks to perform pleasingly parallel (also called embarrassingly parallel) computations. In this paper, we present three pleasingly parallel biomedical applications: (i) assembly of genome fragments; (ii) sequence alignment and similarity search; and (iii) dimension reduction in the analysis of chemical structures, which are implemented utilizing a cloud infrastructure service‐based utility computing models of Amazon Web Services (http://Amazon.com Inc., Seattle, WA, USA) and Microsoft Windows Azure (Microsoft Corp., Redmond, WA, USA) as well as utilizing MapReduce‐based data processing frameworks Apache Hadoop (Apache Software Foundation, Los Angeles, CA, USA) and Microsoft DryadLINQ. We review and compare each of these frameworks, performing a comparative study among them based on performance, cost, and usability. High latency, eventually consistent cloud infrastructure service‐based frameworks that rely on off‐the‐node cloud storage were able to exhibit performance efficiencies and scalability comparable to the MapReduce‐based frameworks with local disk‐based storage for the applications considered. In this paper, we also analyze variations in cost among the different platform choices (e.g., Elastic Compute Cloud instance types), highlighting the importance of selecting an appropriate platform based on the nature of the computation. Copyright © 2011 John Wiley & Sons, Ltd.

Programming the cloud

Client + cloud computing is a disruptive, new computing platform, combining diverse client devices -- PCs, smartphones, sensors, and single-function and embedded devices -- with the unlimited, on-demand computation and data storage offered by cloud computing services such as Amazon's AWS or Microsoft's Windows Azure. As with every advance in computing, programming is a fundamental challenge as client + cloud computing combines many difficult aspects of software development. Systems built for this world are inherently parallel and distributed, run on unreliable hardware, and must be continually available -- a challenging programming model for even the most skilled programmers. How then do ordinary programmers develop software for the Cloud? This talk presents one answer, Orleans, a software framework for building client + cloud applications. Orleans encourages use of simple concurrency patterns that are easy to understand and implement correctly, building on an actor-like model with declarative specification of persistence, replication, and consistency and using lightweight transactions to support the development of reliable and scalable client + cloud software.