A Clouded Future: Analysis of Microsoft Windows Azure As a Platform for Hosting E-Science Applications

Microsoft Windows Azure is Microsoft\u27s cloud based platform for hosting .NET applications. Azure provides a simple, cost effective method for outsourcing application hosting. Windows Azure has caught the eye of researchers in e-science who require parallel computing infrastructures to process mountains of data. Windows Azure offers the same benefits to e-science as it does to other industries. This paper examines the technology behind Azure and analyzes two case studies of e-science projects built on the Windows Azure platform

Extending Office 365 with Microsoft Azure

The topic of this thesis is extending Office 365 with cloud services offered by Microsoft Azure. The purpose of this thesis is to identify and present Azure services which can be used to extend Office 365 functionality. The most essential services and tools are covered from built-in services to custom solutions. In addition to Azure services, Office 365 application programming interfaces are also covered as they can be used with several extension scenarios. Both Office 365 and Azure are rapidly developing cloud platforms that are constantly transforming and offering new features. This thesis will compare cloud solutions to traditional on-premises solutions and will also cover a few upcoming Azure features that can be used to extend Office 365 in the future. Almost all Azure services can be used to extend Office 365 in some way but the study is focused on common Office 365 extension scenarios. General description of each Azure service is given, after which the use cases with Office 365 are specified. This thesis provides an overview on Office 365 extension with a modern cloud computing platform. The extension scenarios are sorted under three topics which describe the usage with Office 365. Several Azure services are covered on some extension scenarios, comparing the options to achieve the most suitable solution for the case. A more profound study is presented of a single specific Office 365 extension scenario.Tässä opinnäytetyössä tutkittiin Office 365 -tuoteperheen laajentamista Microsoftin Azure-pilvipalveluiden avulla. Tavoitteena opinnäytetyössä oli selvittää, mitä Azuren palveluita voidaan käyttää Office 365 -laajentamisessa. Tutkimus on rajattu kaikkein olennaisimpiin Azuren palveluihin. Työssä kuvataan laajennusmahdollisuuksia sisäänrakennetuista palveluista ja työkaluista räätälöityihin ratkaisuihin. Azuren palveluiden lisäksi opinnäytetyössä kuvataan Office 365 -rajapinnat, jotka liittyvät moneen käsiteltävään laajennustapaukseen. Sekä Office 365 että Azure ovat Microsoftin nopeasti kehittyviä pilvipalveluja, jotka muuttuvat koko ajan tarjoten uusia ominaisuuksia. Tämä opinnäytetyö vertaa pilviratkaisuja perinteisiin paikallisiin ratkaisuihin ja esittelee myös muutaman uuden Azure-palvelun, joita voidaan hyödyntää Office 365 -laajentamisessa tulevaisuudessa. Lähes jokaista Azuren palvelua voidaan jollakin tavalla hyödyntää Office 365 -kehityksessä, mutta tutkimuksessa pyrittiin löytämään ratkaisuja yleisimpiin Office 365 -laajennustapauksiin. tarkennetaan palveluun liittyvät Office 365 -käyttötapaukset sekä palvelun käyttö kehitystyössä. Opinnäytetyö antaa kokonaiskuvan Office 365 -laajentamisesta modernin pilvipalvelun kautta. Laajennustapaukset ovat jaoteltu työssä muutaman kokonaisuuden alle. Joidenkin tapausten kohdalla kuvataan useampi Azure-palvelu, joita vertailemalla saadaan selville tapaukseen parhaiten sopiva ratkaisu. Yhden laajentamistapauksen rakentaminen Azure-palvelun avulla kuvataan opinnäytetyössä tarkemmin

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how Azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

Factorization in the Cloud: Integer Factorization Using F# and Windows Azure

Implementations are presented of two common algorithms for integer factorization, Pollard’s “p – 1” method and the SQUFOF method. The algorithms are implemented in the F# language, a functional programming language developed by Microsoft and officially released for the first time in 2010. The algorithms are thoroughly tested on a set of large integers (up to 64 bits in size), running both on a physical machine and a Windows Azure machine instance. Analysis of the relative performance between the two environments indicates comparable performance when taking into account the difference in computing power. Further analysis reveals that the relative performance of the Azure implementation tends to improve as the magnitudes of the integers increase, indicating that such an approach may be suitable for larger, more complex factorization tasks. Finally, several questions are presented for future research, including the performance of F# and related languages for more efficient, parallelizable algorithms, and the relative cost and performance of factorization algorithms in various environments, including physical hardware and commercial cloud computing offerings from the various vendors in the industry

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms.

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

Weather API

Creating a Weather API was a project assigned by Enegia Consulting Oy, a subsidiary of Enegia Group Oy. Enegia Group’s business idea is to offer services to customers helping them to reduce energy related costs. The objective of the project by the Enegia Consulting Oy was to plan and implement Weather API for other Enegia services to consumers. The Weather API would serve weather information to a given postal code, time frame and resolution to all authorized requests to the API. The weather information was gathered from the open data service of the Finnish Meteorological Institute, and stored to the database using Weather API endpoint. The information was gathered daily using Azure WebJob. The Weather API was written with C# using .NET Core 1.0 development platform. The application used time series database InfluxDB to store the weather information. Other data such as the postal code to geolocation mapping was stored to Azure SQL database in Microsoft Azure. The solution ran in Microsoft Azure App services. The Weather API was released to production in May 2017 and since then it has been running continuously without interruption. Further development has been planned, however, the implementation has not yet been started.Säätieto-ohjelmointirajapinta-projekti toteutettiin Enegia Group Oy:n tytäryhtiön Enegia Consulting Oy:n toimeksiannosta. Enegia Groupin liiketoiminta-ajatuksena on tarjota asiakkailleen palveluita, jotka auttavat vähentämään energiakustannuksia. Säätieto-ohjelmointirajapinta-projektin (lyhyemmin Weather API) tavoitteena oli suunnitella ja toteuttaa säätietorajapinta, jota muut Enegian palvelut voisivat käyttää. Weather API palvelisi säätietoja tietylle postinumerolle, aikavälille ja resoluutiolle kaikille valtuutetuille pyynnöille. Säätiedot kerättiin Ilmatieteen laitoksen avoimesta tietopalvelusta ja tallennettiin tietokantaan Weather API:n rajapinnan avulla. Tiedot kerättiin päivittäin Azure WebJobin avulla. Weather API kirjoitettiin C # -ohjelmalla käyttäen .NET Core 1.0 -kehitysalustaa. Sovellus käytti aikasarjatietokanta InfluxDB:tä säätietojen tallentamiseen. Muut tiedot, kuten postinumeron paikoitustieto, tallennettiin Azure SQL -tietokantaan Microsoft Azure -palvelussa. Ratkaisu toteutettiin Microsoft Azure App -palveluissa. Weather API julkaistiin tuotantoon toukokuussa 2017, ja sen jälkeen se on ollut käynnissä keskeytyksettä. Jatkokehittämistä on suunniteltu, mutta toteutusta ei ole vielä aloitettu

CECHY ZARZĄDZANIA KLUCZAMI SZYFROWANIA DANYCH PRZECHOWYWANYCH W CHMURZE MS SQL AZURE

The main principles of data security and access organization in the Microsoft Azure cloud storage are considered. A role of hierarchy and access keys are presented. We describe the setup and the use of their keys (BYOK) for transparent data encryption (TDE) using Azure Key Vault keyring.Uwzględniono główne zasady bezpieczeństwa danych i organizacji dostępu w chmurze Microsoft Azure. Przedstawiono zagadnienia hierarchii ról i kluczy dostępu. Zostały opisane dostosowywanie i używanie własnych kluczy (BYOK) do przezroczystego szyfrowania danych (TDE) przy użyciu magazynu kluczy platformy Azure