Windows Azure Research Articles

Research on Access Control Security in Cloud Computing Environment

With the popularization and development of cloud computing. Safety problem more and more get the attention of the researchers. Especially the access control security is particularly important. In this paper, we give a review of the the access control technology based on cloud computing environment. In the first section, the basic theory of access control theory has been introduced. Then, the paper expounds the main access control model Amazon Cloud platform, Windows Azure, Google Cloud and BaiduCloud in section 2; in section 3 the paper analyzes the mainstream open source cloud platform and different components on access control; in section 4 mainly study the cloud computing environment; In section 5 the paper proposes a system that implements a hypervisor-based access control mechanism. Finally, the advantages and disadvantages of this research are analyzed, and perspectives for further study are also suggested.

Proposed Customized Architecture of Mobile Cloud Computing in Health Care Domain

Nowadays, cloud computing frameworks such as Google AppEngine, Amazon Web Services and Windows Azure platform have become increasingly popular among IT companies and application developers. Concurrently, we have observed a prodigious increase in the usage, deployment and development of smartphone platforms and application around the world. In this paper we are going to discuss the current state of art in the combination of these two popular technologies, known as Mobile Cloud Computing (MCC) and we assume that MCC will be the soul of healthcare revolution. MCC provides advance kind of facilities and amenities for patients and medical personnel. This paper represents the architecture of Mobile Cloud Computing for Health Care Big Data Analysis. Google’s Android operating system based mobile system presented in paper makes healthcare data storage, update and retrieval by using cloud computing.

Towards standard PaaS implementation APIs

Platform as a service (PaaS) supports application developers with the ability to implement and deploy their applications in the cloud. Several heterogeneous PaaS platforms are available, such as Google App Engine (GAE), Windows Azure, Cloud Foundry, and OpenShift. Each PaaS provider has its own proprietary implementation and deployment APIs. The heterogeneity of these APIs makes developers worry about their application portability and interoperability. The work in this paper concerns about the heterogeneity of different PaaS implementation APIs. Standard PaaS implementation APIs, called Std-PaaS APIs, have been proposed to solve the application portability problem. Std-PaaS APIs allow developers to develop generic cloud application by writing their applications once and deploying many times on heterogeneous PaaS providers. Std-PaaS APIs have been evaluated using two case studies, in which generic APIs for cloud persistentstorage service and NoSQL datastore service have been developed and used to developed applications to be deployed onto GAE and Windows Azure.

Towards standard PaaS implementation APIs

Platform as a service (PaaS) supports application developers with the ability to implement and deploy their applications in the cloud. Several heterogeneous PaaS platforms are available, such as Google App Engine (GAE), Windows Azure, Cloud Foundry, and OpenShift. Each PaaS provider has its own proprietary implementation and deployment APIs. The heterogeneity of these APIs makes developers worry about their application portability and interoperability. The work in this paper concerns about the heterogeneity of different PaaS implementation APIs. Standard PaaS implementation APIs, called Std-PaaS APIs, have been proposed to solve the application portability problem. Std-PaaS APIs allow developers to develop generic cloud application by writing their applications once and deploying many times on heterogeneous PaaS providers. Std-PaaS APIs have been evaluated using two case studies, in which generic APIs for cloud persistentstorage service and NoSQL datastore service have been developed and used to developed applications to be deployed onto GAE and Windows Azure.

A framework for integrating wireless sensors and cloud computing

Wireless sensors generate a large volume of data that require a highly scalable framework that enables storage, processing, and analysis. Cloud computing technology can provide unlimited storage in addition to a flexible processing infrastructure, allowing for the management and analysis of vast amounts of sensor data. This paper presents a framework for integrating wireless sensors and cloud computing. This framework can provide scalability and high availability for applications that use wireless sensors. Moreover, this cloud-based framework is designed to immediately make decisions based on real-time sensor and historical data, and a list of sensor and user policies are defined by the system administrator. In order to evaluate the framework performance after applying scalability and availability techniques, a load testing environment was built in the cloud to simulate a large number of virtual users. This environment was created in order to examine the quality of the services as provided by Windows Azure. The results have shown that the use of scalability techniques can significantly increase availability and performance.

A framework for integrating wireless sensors and cloud computing

Wireless sensors generate a large volume of data that require a highly scalable framework that enables storage, processing, and analysis. Cloud computing technology can provide unlimited storage in addition to a flexible processing infrastructure, allowing for the management and analysis of vast amounts of sensor data. This paper presents a framework for integrating wireless sensors and cloud computing. This framework can provide scalability and high availability for applications that use wireless sensors. Moreover, this cloud-based framework is designed to immediately make decisions based on real-time sensor and historical data, and a list of sensor and user policies are defined by the system administrator. In order to evaluate the framework performance after applying scalability and availability techniques, a load testing environment was built in the cloud to simulate a large number of virtual users. This environment was created in order to examine the quality of the services as provided by Windows Azure. The results have shown that the use of scalability techniques can significantly increase availability and performance.

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.

A Secure Mobile Cloud Storage Environment using Encryption Algorithm

Cloud computing frameworks such as, Google App Engine, Amazon Web Services, Windows Azure, and open source frameworks such as OpenStack have become increasingly popular among practitioners. Also, the growth in usage and deployment of smartphone platforms and applications worldwide is increasing rapidly. Mobile cloud computing promotes use of cloud based services in a mobile environment. Data and complex computing modules are processed in clouds and mobile devices do not need a powerful configuration such as CPU speed, and memory capacity. Mobile devices are unable to utilize resources, communication delay, and unexpected mobile vulnerabilities or attacks. These challenges have great effect in the improvement of service qualities of mobile cloud. In this paper, the survey of different vulnerability and attacks on mobile cloud computing identified and also design a secure mobile cloud storage environment through encryption algorithm. The proposed work focuses on solution for the threats that are the major issues for MCC adoption. General Terms Security, smartphone, and encryption.

OICS: A knowledge-based cloud manufacturing system for machine tool industry

In this paper, an Ontology inference cloud service (OICS) with auto-scaling capability is designed and implemented for the CNC machine tool industry. The OICS is a knowledge-based cloud manufacturing system, and is used to recommend machine tools and cutting tools based on the Ontology inference techniques. Three core functional modules: the Ontology inference module, the VMT (Virtual Machine Tool) module, and the request filtering module, are developed to allow multiple users to perform inference service, and verify the recommended machine tools or cutting tools via VMT simulations. The OICS is implemented and hosted in a cloud virtual machine, called a worker. Furthermore, the worker controller (WCR), is designed to automatically adjust the number of virtual machines to provide users splendid service quality. Finally, we deploy the developed OICS to a public cloud platform, namely Windows Azure, to conduct integrated tests. Testing results of a case study physically applying the OICS to a machine tool factory show that the OICS can successfully recommend suitable machine tools and cutting tools for machining tasks, and support multiple users in a reasonable performance. The results of this paper can be a useful reference for industrial practitioners to construct cloudbased manufacturing systems.

Architecture for Software as a Service (SaaS) Model of CBIR on Hybrid Cloud of Microsoft Azure

In various areas of government, academia, hospitals and commerce large collections of digital images are produced. Many of these collections are the merchandise of digitizing existing collections of analogue drawings, photographs, paintings and prints. Generally the only way of searching these collections was by basically browsing or keyword indexing. Digital images databases however, open the way to content-based searching. Content Based Image Retrieval (CBIR) is concerned with the retrieval of images similar to a specified image, from an image repository. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image.This paper proposes a system that can be used for retrieving images related to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. The above features which can be stored in vectors called feature vectors and therefore these are compared to the feature vectors of query image and the image information is sorted in decreasing order of similarity. The processing of the same is done on cloud. The CBIR system is an application built on Windows Azure platform. It is a parallel processing problem where a large set of images have to be operated upon to rank them based on a similarity to a provided query image by the user. Numerous instances of the algorithm run on the virtual machines provided in the Microsoft data centers, which run Windows Azure. Windows Azure Stack is the operating system for the cloud by Microsoft Incorporation. Windows azure Stack is responsible for creating ideal hybrid architecture

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.

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

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

Design and Implementation of Walking Incentive Game Base on Cloud Service

In this article, we focus on the proposal, design, and implementation of a mobile game for walking. Walking is a simple, unobtrusive, and effective sport that can help people stay healthy. Researchers have proposed that games, rather than being simply entertaining, can provide incentive to accomplish work that people are less willing to do. Such a game is called a “serious game.” In our proposed game, the player’s walking steps in the real world are counted as the unique resource and are transformed into their game character’s achievement, called the territory. From an implementation point of view, we chose the iOS platform and Windows Azure cloud service as the front-end and back-end, respectively. On the iOS platform, a pedometer based on the iPhone’s three-dimensional accelerator was developed to count the players’ steps. In addition, Google Maps was used to represent the game situation. Azure was applied as a server to enable multi-user and interaction features. The cloud server maintains low development cost, as well as efficient usability, stability, and scalability.

Study on Architecture and Implementation of Port Logistics Information Service Platform Based on Cloud Computing

This paper analyses service objects and functional positioning of port logistics information service platform firstly. The architecture of port logistics information service platform upon cloud computing is proposed Based on the research of the concept and service mode of cloud computing. The system configuration and function implementation on the basis of Windows Azure Platform is further discussed in detail.

Distance Physical Rehabilitation System Framework with Multi-Kinect Motion Captured Data

Visiting physical therapists to the clinics for physical rehabilitation in regular basis is a very long and timeconsuming trip where the final result for success is truly hard to see in daily training. That’s why technological development in traditional physical rehabilitation system is both important, interesting and its effects on patients’ time-management process is huge. In this paper we have proposed a framework for distance physical rehabilitation system using motion captured data from multiple Kinects which can interact directly with the patients, even grasp and track their movements so as to send those data back to the doctors in clinics using Windows Azure. Its goal is to coach patients through their physical therapy exercises and make those exercises a more enjoyable experience and bring physical therapy alive for them at their homes, the same way doctors interact with them in clinics. General Terms Motion Captured Data, Movement Recognition.

Cloud technologies for educational institutions

Research goals: research cloud solutions that offer educational institutions leading providers in the cloud. Research objectives: to examine the pros and cons of cloud-based tools for use in the educational process of universities. Object of research: universities learning process. Subject of research: the use of cloud technologies in educational process of the university. Research methods used: analysis of publications. Results of the research. Based on the analysis of scientific publications identified the advantages and disadvantages of cloud technologies in education compared with traditional information technology. Studied cloud solutions that offer educational institutions leading providers in the cloud: Microsoft Office 365, Windows Azure Platform, Google Apps Education Edition, jParus – Educational institutions. The main conclusions and recommendations. Cloud technologies have significant promise in the education field.

JetStream: Enabling high throughput live event streaming on multi-site clouds

Scientific and commercial applications operate nowadays on tens of cloud datacenters around the globe, following similar patterns: they aggregate monitoring or sensor data, assess the QoS or run global data mining queries based on inter-site event stream processing. Enabling fast data transfers across geographically distributed sites allows such applications to manage the continuous streams of events in real time and quickly react to changes. However, traditional event processing engines often consider data resources as second-class citizens and support access to data only as a side-effect of computation (i.e. they are not concerned by the transfer of events from their source to the processing site). This is an efficient approach as long as the processing is executed in a single cluster where nodes are interconnected by low latency networks. In a distributed environment, consisting of multiple datacenters, with orders of magnitude differences in capabilities and connected by a WAN, this will undoubtedly lead to significant latency and performance variations. This is namely the challenge we address in this paper, by proposing JetStream, a high performance batch-based streaming middleware for efficient transfers of events between cloud datacenters. JetStream is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites, while at the same time optimizing resource utilization and increasing cost efficiency. The prototype was validated on tens of nodes from US and Europe datacenters of the Windows Azure cloud with synthetic benchmarks and a real-life application monitoring the ALICE experiment at CERN. The results show a 3× increase of the transfer rate using the adaptive multi-route streaming, compared to state of the art solutions.

Conformance testing with ioco proxy-testers: application to web service compositions deployed in clouds

This paper describes a conformance testing method which aims at passively testing the conformance of component-based systems. This paper addresses the problem of reaction sequence observation in implementation environments where the installation of testing tools is not possible. The originality of the method resides in the definition of a proxy-tester model from a specification and in the reformulation of the ioco test relation with proxy-tester properties. Initially, we define the proxy-tester model and we give the theoretical background to automatically generate proxy-testers from specifications modelled with input-output symbolic transition system (IOSTS). The second part of this paper describes the application of this method on web service compositions deployed in Clouds. We provide algorithms and passive tester architectures to collect traces from several concurrent composition instances running in parallel. Finally, we present the testing tool Cloud Paste and experiment results on two Clouds, Google AppEngine and Windows Azure.

OICS: A Knowledge-based Cloud Manufacturing System for Machine Tool Industry

In this paper, an Ontology inference cloud service (OICS) with auto-scaling capability is designed and implemented for the CNC machine tool industry. The OICS is a knowledge-based cloud manufacturing system, and is used to recommend machine tools and cutting tools based on the Ontology inference techniques. Three core functional modules: the Ontology inference module, the VMT (Virtual Machine Tool) module, and the request filtering module, are developed to allow multiple users to perform inference service, and verify the recommended machine tools or cutting tools via VMT simulations. The OICS is implemented and hosted in a cloud virtual machine, called a worker. Furthermore, the worker controller (WCR), is designed to automatically adjust the number of virtual machines to provide users splendid service quality. Finally, we deploy the developed OICS to a public cloud platform, namely Windows Azure, to conduct integrated tests. Testing results of a case study physically applying the OICS to a machine tool factory show that the OICS can successfully recommend suitable machine tools and cutting tools for machining tasks, and support multiple users in a reasonable performance. The results of this paper can be a useful reference for industrial practitioners to construct cloud-based manufacturing systems.

Calibration of SWAT models using the cloud

This paper evaluates a recently created Soil and Water Assessment Tool (SWAT) calibration tool built using the Windows Azure Cloud environment and a parallel version of the Dynamically Dimensioned Search (DDS) calibration method modified to run in Azure. The calibration tool was tested for six model scenarios constructed for three watersheds of increasing size each for a 2 year and 10 year simulation duration. Results show significant speedup in calibration time and, for up to 64 cores, minimal losses in speedup for all watershed sizes and simulation durations. An empirical relationship is presented for estimating the time needed to calibration a SWAT model using the cloud calibration tool as a function of the number of Hydrologic Response Units (HRUs), time steps, and cores used for the calibration.