Windows Azure Research Articles

Privacy-preserving workflow scheduling in geo-distributed data centers

Due to the increasing volume of data to be analyzed and the need for global collaborations, many scientific applications have been deployed in a geo-distributed manner. Scientific workflows provide a good model for running and managing geo-distributed scientific data analytics. However, due to the multi-level data privacy requirements in geo-distributed data centers (DCs), as well as the costly and heterogeneous inter-DC network performance, executing scientific workflows efficiently in such a geo-distributed environment is not easy. In this paper, we propose a privacy-preserving workflow scheduling algorithm named PPPS, which aims at minimizing the inter-DC data transfer time for workflows while satisfying data privacy requirements. We compare PPPS with five state-of-the-art workflow scheduling algorithms using Windows Azure cloud performance traces and real scientific workflows. Experimental results show that PPPS can greatly reduce the workflow execution time compared to the other algorithms by up to 93% while satisfying complicated data privacy constraints.

STAGER: Semantic-Based Framework for Generating Adapters of Service-Based Generic-API for Portable Cloud Applications

In PaaS model, providers have different proprietary APIs, which make developers locked inside a specific platform and not able to easily port their applications among different platforms. So, vendor lock-in problem appeared. One solution to this problem is to use generic APIs with specific adapters. However, any update in a PaaS specific-API makes its corresponding adapter is unusable which causes, what we call, API synchronization problem. Therefore, STAGER (SemanTic-based GenERation of Generic-API Adapters) framework is proposed. STAGER framework provides a semi-automatic adapter generation process, which generates specific adapters of generic APIs for PaaS services (e.g., blob storage and datastore services) for target PaaS platforms. The adapter generation process is based on semantic annotations of the generic APIs and their corresponding PaaS specific-APIs. In order to evaluate STAGER framework, two generic APIs for blob storage and NoSQL datastore services have been proposed. STAGER framework is used to generate the adapters of these generic APIs for two PaaS platforms: Google App Engine (GAE) and Windows Azure. Although there is some overhead for semantically annotating the PaaS APIs, the evaluation results prove the feasibility of STAGER framework and promote the usage of the generated adapters for implementing portable cloud applications.

A Survey on Aneka Cloud Application and Integration with Windows Azure

A Survey on Aneka Cloud Application and Integration with Windows Azure

Mobile lung cancer early warning based on Windows Azure cloud computing

In view of the repeated investment in the current medical lung cancer early warning system, 'information island' and the low rate of lung cancer early warning, this paper proposes a mobile lung cancer early warning system based on Windows Azure cloud computing with the application requirements of medical information system. The system is applied to the smart phones of Windows system, and the Windows Azure Microsoft cloud platform is accessed through wireless network, using SQL Azure to store medical data (such as lung CT image, ECG curve and high-resolution colour ultrasound image), embed the medical lung cancer early warning model based on deep learning in the cloud computing platform to process and analyse the patient's medical data, and realise the real-time synchronous update of doctors, patients and cloud platform data. The accuracy of lung cancer early-warning system on LIDC-IDRI is high, and the detection results are uploaded to doctors and patients' smart phones in real time. The results show that the mobile lung cancer early warning system based on Windows Azure cloud computing not only effectively integrates medical resources, but also provides patients with medical services and consultation at home, which integrates the advantages of all aspects and adapts to the development of the times.

Mobile lung cancer early warning based on Windows Azure cloud computing

In view of the repeated investment in the current medical lung cancer early warning system, 'information island' and the low rate of lung cancer early warning, this paper proposes a mobile lung cancer early warning system based on Windows Azure cloud computing with the application requirements of medical information system. The system is applied to the smart phones of Windows system, and the Windows Azure Microsoft cloud platform is accessed through wireless network, using SQL Azure to store medical data (such as lung CT image, ECG curve and high-resolution colour ultrasound image), embed the medical lung cancer early warning model based on deep learning in the cloud computing platform to process and analyse the patient's medical data, and realise the real-time synchronous update of doctors, patients and cloud platform data. The accuracy of lung cancer early-warning system on LIDC-IDRI is high, and the detection results are uploaded to doctors and patients' smart phones in real time. The results show that the mobile lung cancer early warning system based on Windows Azure cloud computing not only effectively integrates medical resources, but also provides patients with medical services and consultation at home, which integrates the advantages of all aspects and adapts to the development of the times.

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.

Windows Azure: перспективы и недостатки использования в образовательном процессе подготовки инженеров-программистов

Windows Azure: перспективы и недостатки использования в образовательном процессе подготовки инженеров-программистов

Enhancing and Developing Multi Block Proxy Re-encryption Methodology in Network Security

Windows Azure Storage (WAS) is a scattered accumulating structure that empowers clients to store clearly boundless tends of information for any term of time. Gigantic safety is not given internet to content prospering. Internet storage device separates record into social events and contemplated security. Those encoded gatherings placed on emulate storage device assistance. The mixed contents changed over converted formats included esteem byte execution getting to the essential content. The internet device makes correspondence included mixed contents showed up unmistakably in group with the etching outfitted with the access to confirm the content ordinariness. The code is executed for the back-up of the data. The encryption arrangement of the contents done by the content owner before it accomplishes internet storage. This ensures veritable twofold clock encryption. In the paper we deal with the existing system we deals with. Server used for storing data. Single Content are uploaded into the different size in different server. That is inability to any information in a server influence the entire framework. Along these lines all content setback from the server. In the wake of utilizing different server this issue could be stayed away from. The encryption plots in the current framework are not compelling in light of the fact that just couples of activities are upheld over scrambled information. In the event that at all security exists, the outsider evaluator ought to be permitted to get to the whole information bundles for confirmation. Internet encounters a ceaseless disillusionment escape contents ground-breaking support execution present structure. This framework, Internet storage will part of document converts same size, thought about security. These relating encoded groups placed in various Internet storage, its passwords are dissipated in various main devices. That encoded information are changed over formats included consistency execution bind access getting to the primary content by utilizing AES, MD5 ALGORITHM if the considerable customer getting to the content cloud will recoup the contents reversible way.

Performance Analysis of Load Balancing Algorithms using Microsoft Windows Azure Cloud Platform

As we are living in technology dominating era, the role of cloud computing as one of the rapidly emerging technologies is highly indispensable. This cloud computing holds load distribution concept, which is usually addressed as a challenging phenomenon, to handle multiple requests which are coming from geographically differentiated locations. There are various load balancing mechanisms exits for the effective distribution of the work load. This load balancing mechanism takes the advent of the virtualization approach on resources to optimize the utilization of the resources. In virtualization, the creation of virtual machine and mapping the same to the suitable request makes the cloud computing a prominent technology. This paper work considers various load balancing mechanisms for the allocation of resources and also shows a comparative study on them with the help of an example scenario. These load balancing algorithms are applied on applications like real-time systems and prioritized medical field related data storage system. This research work is implemented in Windows Azure Platform as Cloud Operation System under load balancing component.

Privacy Regulation Aware Process Mapping in Geo-Distributed Cloud Data Centers

Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways of mapping parallel processes to physical nodes (i.e., “process mapping”) could significantly impact the performance of the applications because of non-uniform communication cost in geo-distributed environments. What's more, the different data privacy requirements in geo-distributed data centers pose additional constraints on process mapping solutions. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environment, which is a challenging task due to the multi-level data privacy constraints, heterogeneous network performance and process failures. In this paper, we introduce the special privacy requirements in geo-distributed data centers and formulate the geo-distributed process mapping problem as an optimization problem with multiple constraints. We develop a new method to efficiently find good process mapping solutions to the problem. Experimental results on real clouds (including Amazon EC2 and Windows Azure) and simulations demonstrate that our proposed approach can achieve significant performance improvement compared to the state-of-the-art algorithms.

FEATURES OF USING CLOUD TECHNOLOGIES IN EDUCATION

The article investigates cloud technologies and features of their use in educational institutions. The essence of cloud technologies in education is determined. It is noted that the modern institution of higher education provides usage of information and communication technologies, and in particular, cloud technologies. The main characteristics (features) which characterize cloud services are described here. It is noted that usage of cloud services has benefits and problems as well. There are such mandatory characteristics of cloud computing as self-service on demand, universal access to the network, resource pooling, elasticity, consumption accounting. Four models of deployment of cloud technologies are revealed in the article. They are corporate, public, group and hybrid. It is admitted that the most appropriate model of deployment of cloud technologies in the infrastructure of higher education institutions is hybrid. Cloud technologies support such activities as: communication, collaboration and co-operation, which determines certain areas of their usage. The architecture of cloud technologies is presented in three levels: software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS). The most popular cloud services which are used in various spheres, including educational institutions, are analyzed here. Office 365, Microsoft Planner, Microsoft SkyDrive, Google Drive, Google Talk, Google Docs, these are the common cloud services which are selected in SaaS. Popular software platforms which are presented in the PaaS segment are Windows Azure, Google App Engine, Cloud9 IDE, Ideone IDE. Each of the cloud platforms that is described in the article has its own peculiarities. Possible areas of application of cloud technologies in the educational process are identified. They are free access to the programs and services used in the educational process, planning activities, implementation of the working regime of teachers and employees, the organization and conduct of scientific online conferences, seminars, forums, round tables, trainings, joint project work of students, individual independent work of students, electronic interaction with entrants.

A COMPARATIVE STUDY ON GOOGLE APP ENGINE AMAZON WEB SERVICES AND MICROSOFT WINDOWS AZURE

In today Internet has grown to be persistent in daily livelihood furthermore Cloud computing is a rising model where computing resources offered over the Internet as scalable, on-demand (Web) services. An association deploy internet service needs to use enormous amounts of money on infrastructure needs to serve feasible users which is not a problem for large venture but when it comes to Small and Medium Enterprises or Enterprises affordability becomes a huge factor with the huge infrastructure come problems like machines failure, hard drive noises, software bugs, etc. Here might be a big problem for such a community. Cloud Computing is the ultimate solution to this problem. Rather than buying, installing and operating its own systems, an organization can rely on a cloud provider to do this for them. Cloud Computing key market leaders like Google, Amazon and Microsoft etc, these providers introduce new operating and business models that allow customers to pay for the resources they completely use, instead of making tremendous upfront investments. The purpose of this paper is to analyze most popular platforms, The Google App Engine, Amazon Web Services, and Windows Azure Platform.

A Benchmarking Tool for Elastic MQTT Brokers in IoT Applications

Cloud computing is an evolution in IT consumption and delivery which makes available self-management on the Internet with a flexible, pay-as-you-go business model. Within the context of Internet of Things, the MQTT (Message Queuing Telemetry Transport) protocol that is implemented broadly by the applications of “Publish-Subscribe” paradigm has a vital role. However, MQTT brokers are saturated easily if they have to cope with huge and speedy data generated by IoT “chatty” devices. With capability of provisioning/deprovisioning granular virtual resources, Cloud computing empowered MQTT brokers by enabling its elasticity feature. Elasticity helps the brokers deal with a very large variety of data integrated into the IoT every single day. However, there was lack of sturdy benchmarking tools that judge all the aspects of MQTT brokers in order to advocate correct elastic decision-making. This article focuses on the work of benchmarking MQTT by introducing a new developed tool called MQTTBrokerBench. With this tool, users not only can benchmark MQTT brokers but also can specify saturation points where the IoT load makes the brokers be saturated. Those saturation points can be used to set thresholds for elastic decision-making. Furthermore, the article also demonstrates the results acquired by this tool through the experiments on Windows Azure Cloud Platform.

Design and Implementation of Authentication as a Service (Aaas) in Windows Azure Cloud Platform

Cloud computing is an internet based computing. It consists of various services and delievers on demand basis from the user request. Data security is a necessary facet for any web applications. Authentication is an important security problem in cloud as well as internet facing applications. This paper proposes authentication algorithms for securing the cloud applications. Authentication is an essential factor among other security parameters such as authorization, auditing and availability. These proposed algorithms are used to protect the unauthorized access in cloud. User’s data are converted into different form using the different encryption techniques. These three algorithms are combined and provide as a single service to the cloud user. Proposed User authentication mechanisms are designed and developed in ASP.NET platform and deployed on Windows Azure platform of cloud System. This user authentication mechanism is simply matched with any kind of service within the cloud environment. The proposed user authentication algorithms have provided best security in cloud system. This mechanism can be used for agriculture, medical, education and IT based applications to firmly access the information in secured manner.

On the Average Locality of Locally Repairable Codes

A linear block code with dimension $k$ , length $n$ , and minimum distance $d$ is called a locally repairable code (LRC) with locality $r$ , if it can retrieve any coded symbol by at most $r$ other coded symbols. LRCs have been recently proposed and used in practice in distributed storage systems, such as Windows Azure Storage and Facebook HDFS-RAID. Theoretical bounds on the maximum locality of LRCs ( $r$ ) have been established. The average locality of an LRC ( $\overline {r}$ ) directly affects the costly repair bandwidth, disk I/O, and the number of nodes involved in the repair process of a missing data block. There is a gap in the literature studying $\overline {r}$ . In this paper, we establish a lower bound on $\overline {r}$ of arbitrary $(n,k,d)$ LRCs. Furthermore, we obtain a tight lower bound on $\overline {r}$ for a practical case where the code rate $(R=({k}/{n}))$ is greater than $(1-({1}/{\sqrt {n}}))^{2}$ . Finally, we design three classes of LRCs that achieve the obtained bounds on $\overline {r}$ . Comparing with the existing LRCs, our proposed codes improve the average locality without sacrificing such crucial parameters as the code rate or minimum distance.

A new model for cloud elastic services efficiency

ABSTRACTThe speedup measures the improvement in performance when the computational resources are being scaled. The efficiency, on the other side, provides the ratio between the achieved speedup and the number of scaled computational resources (processors). Both parameters (speedup and efficiency), which are defined according to Amdahl’s Law, provide very important information about performance of a computer system with scaled resources compared with a computer system with a single processor. However, as cloud elastic services’ load is variable, apart of the scaled resources, it is vital to analyse the load in order to determine which system is more effective and efficient. Unfortunately, both the speedup and efficiency are not sufficient enough for proper modeling of cloud elastic services, as the assumptions for both the speedup and efficiency are that the system’s resources are scaled, while the load is constant. In this paper, we extend the scaling of resources and define two additional scaled systems by (i) scaling the load and (ii) scaling both the load and resources. We introduce a model to determine the efficiency for each scaled system, which can be used to compare the efficiencies of all scaled systems, regardless if they are scaled in terms of load or resources. We have evaluated the model by using Windows Azure and the experimental results confirm the theoretical analysis. Although one can argue that web services are scalable and comply with Gustafson’s Law only, we provide a taxonomy that classifies scaled systems based on the compliance with both the Amdahl’s and Gustafson’s laws.For three different scaled systems (scaled resources R, scaled load L and combination RL), we introduce a model to determine the scaling efficiency. Our model extends the current definition of efficiency according to Amdahl’s Law, which assumes scaling the resources, and not the load.

Performance Evaluation of Cloud Computing Resources

Cloud computing is an emerging information technology which is rapidly growing. However, measuring the performance of cloud based applications in real environments is a challenging task for research as well as business community. In this work, we focused on Infrastructure as a Service (IaaS) facility of cloud computing. We made a performance evaluation of two renowned public and private cloud platforms. Several performance metrics such as integer, floating Point, GFLOPS, read, random Read, write, random write, bandwidth, jitter and throughput were used to analyze the performance of cloud resources. The motive of this analysis is to help cloud providers to adjust their data center parameters under different working conditions as well as cloud customers to monitor their hired resources. We analyzed and compared the performance of OpenStack and Windows Azure platforms by considering resources like CPU, memory, disk and network in a real cloud setup. In order to evaluate each feature, we used related benchmarks, for example, Geekbench & LINPACK for CPU performance, RAMspeed & STREAM for memory performance, IOzone for disk performance and Iperf for network performance. Our experimental results showed that the performance of both clouds is almost same; however, OpenStack seems to be better option as compared to Windows Azur keeping in view its cost as well as network performance.

Cost Aware Resource Selection in IaaS Clouds

One of the main challenges in cloud computing is to cope up with the selection of efficient resources in terms of cost. There are various cloud computing service providers which dynamically provide resources to the customers through different pricing policies. Based on the different APIs and pricing policies of the service providers, it becomes difficult for the customers to select the best service provider in terms of cost. In some cases, if the usage of the resources provided by a datacenter exceeds certain limit, then the providers cannot offer more resources to the customers as new VMs cannot be created. Hence, even if the customer chooses the best provider based on the least cost parameter, still there is no guarantee that the provider allocates complete resources to the customer. For this reason, I present system architecture that selects the best service provider based on the customer requirements mainly the cost. The proposed architecture also performs resource management by automatically providing new VMs from the available service providers in the inter cloud. The proposed system is based on five clouds i.e. Amazon EC2, Cloudsigma, Google, GoGrid, and Windows Azure. An interface is designed for obtaining the user requirements. These user requirements are matched with the design database of five cloud providers and based on the matched values; the catalog of optimal costs for each particular cloud is shown to the user. Then Cost Aware Resource Selection algorithm is run for determining the lowest optimal cost for Instance based approach and Quantity based approach. The algorithm tackles two domains of clouds for the algorithm i.e. Single Cloud and Multi Cloud.

An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers

It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service (ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit. We also propose several algorithms to enhance the cost efficient and SLO guarantee performance of ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> including i) dynamic request redirection, ii) grouped Gets for cost reduction, iii) lazy update for cost-efficient Puts, and iv) concurrent requests for rigid Get SLO guarantee. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> in payment cost minimization and SLO guarantee in comparison with previous methods.

Minimum-Cost Cloud Storage Service Across Multiple Cloud Providers

Many cloud service providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different get/put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs’ datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: 1) how to allocate data to worldwide datacenters to satisfy application service level objective (SLO) requirements, including both data retrieval latency and availability and2) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using the integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost-based data allocation algorithm and an optimal resource reservation algorithm. We further propose three enhancement methods to reduce the payment cost and service latency: 1) coefficient-based data reallocation; 2) multicast-based data transferring; and 3) request redirection-based congestion control. We finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage, and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.