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This study looked at Platform as a Service and information service delivery in South-South and South-East federal university libraries in Nigeria. To direct the study, one research question was created, and one hypothesis was tested at .05 level of significance. For the study, a descriptive survey design was used, and 117 employees of the institutions under study's who work in the electronic library made up the study's population. Therefore, the study used a census sample method. Data for the study were gathered using a standardised questionnaire called the "Platform as a Service and Information Service Delivery Questionnaire (PSISD)". The data were examined using frequency distribution, percentages, and linear regression. According to the research, Open skies, Serial Solution (ProQuest), Future of Library is Open (FOLIO), Alma/Ex Libris, Online Computer Library Centre (OCLC), Polaris integrated library system, and Ebsco are among the Platform as a Service (PaaS) used in university libraries in South-South and South-East, Nigeria for the delivery of information services. University libraries in South-South and South-East, Nigeria are able to give fast and real-time information services because to the deployment of Platform as a Service. The writers derived and examined the majority of the study's findings and outcomes. This study is unique in the field of cloud computing. As a result, the document will potentially be used as a reference source by library administration, library patrons, researchers, and the wider academic community.

5G realizes an impactful convergence, where Network Functions Virtualization (NFV) and cloud-native models become fundamental for profiting from the unprecedented capacity offered at the 5G Radio Access Network (RAN). For providing scalability and automation management over resources in 5G infrastructure, cloud-native and Platform as a service (PaaS) are proposed as solutions for paving the way for vertical applications in 5G. This paper leverages cloud-native models, PaaS, and virtual testbed instances to provide key platform provisioning and service life-cycle management features to a selected User Generated Content (UGC) scenario in multimedia applications. Specifically, this article and results show how service-level telemetry from a UGC cloud-native application is used to automatically scale system resources across the NFV infrastructure., Previous version of the paper is accepted in IEEE Future Networks World Forum (FNWF), Montreal, 2022

As the world goes digital and millions of devices connect to form the Internet of Things, IoT hacks are becoming a worrisome trend. These potentially devastating security breaches make strong IoT security an imperative for companies. Novel approaches like communications platform as a service (CPaaS) and secure access service edge (SASE) can help companies keep their connected devices secure.

Social media platforms display unique qualities that afford unanticipated uses and challenge the application of popular human-centered evaluations and interpretations. This observation, coupled with a rise in unanticipated uses, demonstrates that the design, function, and use of platforms are best treated through a sociotechnical lens.

In recent years, cloud computing had a great impact on technology. Cloud computing has formed a conceptual and infrastructural basis for future computing. It provides services like Software as a Service, Platform as a Service and Infrastructure as a Service which can be deployed on / by private cloud, public cloud, community cloud or hybrid cloud. With the number of advantages of this technology, it comes with various challenges and issues simultaneously. From the challenges, security is considered the most critical one as the data is stored on the remote locations with the help of the Internet instead of using hard disks. This paper firstly discusses the basics of cloud computing, its service models and deployment model and then focuses on cloud security and issues related to it.

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable.

As the world is digitalizing in the fast pace, there is a need for more reliable and scalable integration management. One popular option for modern integration management has been Integration Platforms as a Service (iPaaS). iPaaS is usually implemented to replace the old integration implementation models such as the point-to-point integrations or enterprise service buss. In this research, we are focusing on the state of the iPaaS themed research in the academic field. We also investigate what kind of different viewpoints or research gaps there are in the iPaaS research and how the term iPaaS has evolved and become more clearer during the years. In the results of our systematic literature review (n=14), we reveal that the majority of the iPaaS research has been focused on technological approach. However, now there are signs that iPaaS researchers are beginning to emphasize business-intensive approach, such as the importance of integration strategy or the selection of the right iPaaS platform to serve the enterprises business needs. This study calls for the further work in the area of integration management research. CCS CONCEPTS • Information systems → Enterprise applications; Enterprise information systems. Post-print / Final draft

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications’ components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.

Opportunities of healthcare informatization tools are closely related to and limited by requirements of the corresponding model of care. The paper covers specific features of biopsychosocial patient-centered (BPC) model and their implications for informatization. Some approaches to automation of BPC model are discussed: ecosystem architecture and patient-centered online platform that provides communication infrastructure for consumers and providers of health services. Operation principles, functions and services of the platform are described that should help the platform meet the requirements of BPC model. The proposed online platform can supplement the existing healthcare informatization tools in addressing the challenge of chronic diseases epidemic.

Over the recent decade the world has seen an increase in businesses launching new, or changing theirbusiness model to, digital platforms. New and established businesses are flocking to digital platformsin order to evolve their business model and keep up with advancements in technology, such as cloudcomputing, which enables commerce and communication on a much faster and more streamlinedlevel. Digital platforms with two-sided markets often face fierce competition from market incumbentswhich benefit from traditional supply-side economies of scale, as well as from other digital platforms.Therefore, the competitive strategy adopted at market launch and under operations will have a greatimpact on the platform performance in terms of firm financial performance.This study is divided into two parts and is performed with the objective to gain insight into thecompetitive strategies adopted by digital platform businesses with two-sided markets, and how suchstrategic decisions may be informed in favor of profitability. The first part investigates the influence ofinternal factors, such as debt ratio, quick ratio, sales growth, and capital turnover ratio, on the firmfinancial performance (measured by return on assets) of digital platforms with two-sided markets inthe Nordics. The second part investigates the relationship between the firm financial performance(measured by return on assets) of digital platform businesses with two-sided markets after launch andthe type of strategy adopted. Subsequently, two hypotheses are presented. Subsequently, twohypotheses are presented. A panel data regression model is developed to evaluate these relationships,allowing the authors to test the null hypotheses. The data set used in the panel data regression modelcomprise an unbalanced sample of 27 companies who have launched their platforms in Norway,Sweden, and Denmark. Financial data was gathered in the form of return on assets (dependentvariable), capital turnover ratio, quick ratio, debt ratio, and sales growth (explanatory variables).These companies were grouped depending on which strategy was adopted on market launch and underearly operations. These strategies are subsidy, seeding and marquee users, micro market launch andpiggybacking (categorical ‘dummy’ variables).Studying the firm financial performance of businesses which adopt digital platforms will help us tobetter understand the efficacy of strategies adopted and how these strategies impact financialperformance. Both null hypotheses tested may be partially rejected. The authors conclude that theinternal factors debt ratio, quick ratio, and sales growth have a significant influence on theprofitability (measured by return on assets) of digital platforms with two-sided markets in the Nordics.The influence of the internal factor capital turnover ratio on profitability is statistically insignificant.Quick ratio has a positive significant influence on profitability, whereas debt ratio and sales growthhave a negative influence. The authors also conclude that companies which have business modelsallowing them to adopt a subsidy strategy yields stronger profitability than those which adopt otherstrategies. Companies which entice seed & marquee users to their platform as a strategy yields thesecond strongest profitability. Companies which choose a micro market launch strategy yield theweakest profitability. The authors of this study will not draw conclusions on the efficacy of theadoption of a piggybacking strategy on profitability due to the limited number of observationsattributed to the piggybacking dataset.Future studies may expand upon this research with the inclusion of a wider catchment of businesses,as well as the inclusion of a wider data set to include other geographical locations and improvestatistical significance of the data set. An improvement to the study may also be to analyze thecorrelation between the strength of competitors upon market entry and the efficacy of the strategiesadopted.

The Agave Platform is a Science-as-a-Service (ScaaS) platform for reproducible science. The current production deployments of the platform are deployed and managed using Ansible and Docker Compose. While capable, this has historically led to operational complexity for those adopting the platform. Over the last year, we have worked to migrate the platform to a Kubernetes native deployment. In this paper we discuss our experiences evolving the platform, its architecture, and getting the most out of the Jetstream cloud.

To overcome huge resource consumption of neural networks training, MLaaS (Machine Learning as a Service) has become an irresistible trend, just like SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service) have been. But it comes with some security issues of untrustworthy third-party services. Especially machine learning providers may deploy trojan backdoors in provided models for the pursuit of extra profit or other illegal purposes. Against the redundant nodes-based trojaning attack on neural networks, we proposed a novel detecting method, which only requires the untrusted model to be tested and a small batch of legitimate dataset. By comparing different processes of neural networks training, we found that the embedding of malicious nodes will make their parameter configuration abnormal. Moreover, by analysing the cost distribution of test dataset on network nodes, we successfully detect the trojaned nodes in the neural networks. As far as we know, the research on the defence against trojaning attack on neural networks is still in its infancy, and our research may shed light on the security of MLaaS in real-life scenarios.

International audience; Cloud computing has emerged as the main paradigm for hosting and delivering computing resources as services over Internet. It provides a delivery model for computing resources at infrastructure, platform, and software levels. However, there is a plethora of cloud providers offering different resource management interfaces. Due to this diversity, the consumption, provisioning, management, and supervision of cloud resources are subjected to four key issues, i.e., heterogeneity, interoperability, integration, and portability. To cope with these issues, Open Cloud Computing Interface (OCCI) is proposed as a community-based and open recommendation standard for managing any kind of cloud resources. Currently, only runtime implementations exist for OCCI, and each one targets a specific cloud service model such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS). Thus, OCCI lacks an approach to model and execute different OCCI artifacts. Our approach provides a generic modeling framework coupled with a generic runtime implementation. In this article, we propose the OCCIware approach, which represents the first approach to design, validate, generate, implement, deploy, execute, and supervise everything as a service with OCCI. This approach provides OCCIware Studio, the first model-driven tool chain for OCCI. It is built around OCCIware Metamodel, which defines the static semantics for the OCCI standard in Ecore and OCL. In addition, it proposes OCCIware Runtime, the first generic OCCI runtime implementation targeting all the cloud service models (IaaS, PaaS, and SaaS). OCCIware provides a unique and unified framework to manage OCCI artifacts and, at the same time, it represents a factory to build cloud domain-specific modeling frameworks where each framework targets a specific cloud domain. OCCIware has been applied in various cloud domains and use-cases, which validate its applicability.

Bonus computing is a new metacomputing form that takes advantage of free computing power from the public Cloud market. To maximize the value of free Cloud resources and facilitate dividing Bonus computing tasks, it would particularly be crucial to understand the performance of candidate Cloud services before using them in production. By offering free quotas in its standard environment, Google App Engine (GAE) has become a popular public Platform-as-a-Service (PaaS) for Bonus computing. Since GAE natively supports various programming languages with flexible configurations (e.g., region selection), it will be possible and valuable to squeeze GAE’s free computing power if there is an optimal choice of its different runtimes. Following the performance evaluation methodology DoKnowMe, we implemented several versions of the Fibonacci(-like) calculation as benchmarks to fundamentally investigate GAE’s standard environment. Our investigation results reveal that GAE does not support its runtime environments homogeneously in terms of their computation speed and memory efficiency. The heterogeneity could be related not only to the characteristics of different programming languages but also to the diverse GAE infrastructures. For example, Go runtime seems to be a well-trade-off to satisfy Bonus computing among all the options, while the GAE service located in southamerica-east1 and us-central1 performs dramatically worse than that in the other regions.

Network platform as a Service (NPaaS) is an emerging concept in the telecommunications industry, in which services are deployed across a multitude of communication service provider (CSP) networks. In addition to connectivity requirements, these services will have associated and varied security and performance requirements. While such requirements must be verified across multiple CSP networks, CSPs will be selective about the degree of network information and guarantees they are willing to provide to NPaaS providers, application service providers, and other CSPs. Furthermore, all such providers are likely to be wary about network information or guarantees provided by CSPs with whom they do not have trust relationships. In this short paper, we present some challenges facing NPaaS security and performance, and demonstrate through an example case study how recent formal network verification techniques and the open-source network verification toolset NV can be used as a framework to verify security and performance requirements in an NPaaS context.

The adoption of cloud computing has been one of the major and most influential shifts in our industry in the past decade, and shows no sign of abating. The power and ubiquity of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings have transformed the way that much of our software is built, deployed, and accessed. However, we don’t believe that these platforms have yet had a similarly broad and significant impact on security. While these platforms do have sophisticated and powerful access control models, they’re largely designed to protect services based within their cloud environments, and not serve as broad enterprise security solutions for all users across multiple heterogeneous environments.

This chapter examines the evolution of cloud computing and how the history of computing has swung between remote and local computers. It uses NIST definition to examine five essential features of any cloud computing service. Then the three service models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are examined. Furthermore, we look into the deployment models of private, community, public, and hybrid clouds. Next, we will study the value-added chain of various cloud stakeholders, including their pain points. Lastly, a case will be made for conducting AI and ML in cloud. It will include a workflow for creating an ML model, with some real-life examples. We wrap up this chapter by looking at the future trends that include targeted hardware.

This research paper explores the capabilities of Software as a Service (SaaS) to provide a cost-efficient and reliable start for any upcoming industry while considering its implications, advantages, disadvantages, growth, and economic aspect. Cloud computing is a form of “Internet Computing” for the use of computational power over the Internet. With cloud computing, end users can take advantage of computer assets that are deployed over the Internet. Cloud provides three basic services: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS provides a platform for online deployment of frameworks that helps to reduce time and assets spent unlike grid computing.

In this chapter, we discuss protecting platform-as-a-service workloads. After reading this chapter, you will be able select and implement appropriate controls for securing platform-as-a-service workloads as part of your organization’s security policy framework.

With the change in the lifestyle and food habits of the people nowadays, the heart-related diseases or the cardiovascular diseases (CVDs) are increasing among the population not just in India but around the globe. There is always a need of accurate and reliable predicting system for these life-threatening diseases in time for proper treatment. The healthcare industry is producing a large amount of data but it is not always made use to the full extent and is often underutilized. Various machine learning (ML) algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex datasets which in turn can detect, predict and even cure such diseases. The medical data such as heart rate, blood pressure, cholesterol and so on can be used as input and these features are modelled for classification. The data like blood pressure and heart rate are being taken from the user through the sensor on a wearable device that sends the data in real time to the cloud database. ML Platform as a Service (PaaS) hosts custom made ML model that is trained using several algorithms to pick the best accurate one. The classification results are showed to the user on his/her wearable device which is very ergonomic. This system can be used by normal people of all ages or patients who are concerned about health.

With the rise of the Industrial Internet of Things (IIoT), there is an intense pressure on resource and performance optimization leveraging on existing technologies, such as Software Defined Networking (SDN), edge computing, and container orchestration. Industry 4.0 emphasizes the importance of lean and efficient operations for sustainable manufacturing. Achieving this goal would require engineers to consider all layers of the system, from hardware to software, and optimizing for resource efficiency at all levels. This emphasizes the need for container-based virtualization tools such as Docker and Kubernetes, offering Platform as a Service (PaaS), while simultaneously leveraging on edge technologies to reduce related latencies. For network management, SDN is poised to offer a cost-effective and dynamic scalability solution by customizing packet handling for various edge applications and services. In this paper, we investigate the energy and latency trade-offs involved in combining these technologies for industrial applications. As a use case, we emulate a 3D-drone-based monitoring system aimed at providing real-time visual monitoring of industrial automation. We compare a native implementation to a containerized implementation where video processing is orchestrated while streaming is handled by an external UE representing the IIoT device. We compare these two scenarios for energy utilization, latency, and responsiveness. Our test results show that only roughly 16 percent of the total power consumption happens on the mobile node when orchestrated. Virtualization adds up about 4.5 percent of the total power consumption while the latency difference between the two approaches becomes negligible after the streaming session is initialized.

Cloud computing has transformed the way IT services are provisioned and consumed, shifting the burden of configuration and management to the cloud provider. A lot of research has been conducted in the field aimed at optimizing and/or devise new in-cloud service delivery models. Yet, service outages are a norm rather than an exception. A natural evolution for application developers, especially for those applications that must meet high availability requirements, is to rely on resources and services provisioned by multiple cloud simultaneously. The so-called multi-cloud, distributed deployment model coupled with a monitoring solution spanning multiple domains could help in a timely identification and isolation of faulty components, alleviating service impairment issues. To this end, we propose an extension to NoMISHAP, a Platform as a Service (PaaS) multi-cloud middleware, introducing a cross layer monitoring solution for use in distributed cloud environments. Experimental results show the effectiveness of our approach and its ease of adoption for management tasks.

Academic researchers who are performing computational-heavy research may be at a disadvantage if their available systems are not suitable enough or may take a lot of time to produce a result set. Some academic institutions might not have the necessary resources to address the research needs. In this research, we extend our initial concept of clustering Raspberry Pi systems to investigate the viability and requirements needed to utilise school computers to create a Platform as a Service that can serve the needs for undergraduate researchers. A computer cluster utilising twenty-five school mid-level computers was created with a novel quality of service offering and bench-marked for performance while also creating a platform for students to be able to submit multiple neural network models to train them concurrently by utilising Distributed TensorFlow. It was concluded that low-to-medium end computers could be used to create a computer cluster for research purposes, yet a number of factors need to be taken into consideration.

The first mention of Azure SQL, referred to as “Microsoft SQL Services” at the time, was when Microsoft announced “Project Red Dog” (Microsoft Azure) at Microsoft’s Professional Developer Conference (PDC) in 2008. This cloud-hosted version of SQL Server was one of the first services available on Azure. From the beginning, it was meant to provide a Platform as a Service (PaaS) offering of SQL Server, where you don’t have to provision hardware or patch software and you can enjoy benefits that come with running in the cloud – availability, performance, security, and scale – without deploying complex and expensive systems on premises. Since then, Azure SQL has continued to grow and evolve with the Azure platform. As you read this book, it’s important to remember that things are always changing. While some of the “little rocks” or details throughout the book may change slightly as the platform evolves, the “big rocks” you will learn in this book will provide a solid foundation for you to grow in the space of developing with databases.

Many organizations, driven by the need for greater productivity and lower costs, are moving their portfolio of applications to the cloud. However, there are many challenges in migrating an application to the cloud. That is, there are different aspects of an application that affect its cloud-migration feasibility. That is why assessing the feasibility of migrating an application to the cloud is not an easy task. Also, it is non-trivial to map which cloud solution - whether infrastructure as a service, platform as a service, or software as a service, should be used for hosting the application, if it is shown to be cloud-migration feasible. In this paper, we present a novel cloud-migration feasibility advisor that analyzes the meta data received about an application and evaluates whether it is feasible to move it to the cloud or not. In addition, the advisor uses dependency graphs and the aforementioned meta data to classify which cloud solution would suit best for the application, if it is deemed migration feasible.

Cloud computing services like software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are delivery models which provides software, application platform, and infrastructure recourses to consumer respectively. This paper presents service models, deployment models, and essential characteristics of cloud computing and cloud security and privacy issues. This paper also describes detailed literature on cloud security issues and layerwise classification of each issue. The various vulnerabilities, attacks, and defense mechanisms are provided for securing cloud environment

Security is one of the key considerations when businesses look to adopt cloud models like Platform as a Service (PaaS). The main concern comes from the fact that business losses total control of deployed applications and data, leaving that trust in the hands of the Cloud Service Providers (CSP). Although CSPs put some level of security controls in place, it is ultimately the responsibility of the client to verify the presence of the controls and verify the suitability of their security needs. It is also the user's responsibility to ascertain compliance with these offerings to appropriate standards before patronage. This paper reviewed various security issues inherent in the PaaS cloud model, classified them according to the essential cloud characteristics and finally recommended high-level solutions to the identified security issues. The result of the review and recommendations provided can serve as a reference for organizations and individual users when deciding security needs and service level objectives for PaaS based deployments.

Las empresas almacenan cada vez más información en sus sistemas sin ser plenamente conscientes de su valor y la facilidad con la que pueden perderla. Desde el daño físico de sus servidores, discos duros y medios de almacenamiento, como otros factores como las inundaciones o sobrecargas eléctricas, pasando por ataques informáticos, secuestro de archivos o infecciones de virus, simples caídas o borrado accidental o intencionado de datos, son muchas las causas de la pérdida de información por parte de las pymes. Dentro de los impactos que tendría una pymes ante un desastre tecnológico esta Impacto directo en los beneficios y en el futuro del negocio, en la reputación de la marca, Problemas legales con las normativas de protección de datos, Costes directos elevados, Falta de disponibilidad y accesibilidad a los datos. Este artículo presenta una visión general de los conceptos más importantes de la Computación en la nube mostrando las potencialidades que tiene el servicio de almacenamiento a bajo costo de Amazon S3, se realiza una comparativo entre una implementación tradicional On premise y el servicio de almacenamiento S3 a nivel de seguridad, Disponibilidad y Costos, posteriormente finaliza con el análisis de una caso estudio utilizando el modelo financiero de Coste Total de Propiedad (TCO), como herramienta para toma decisiones en proyectos de tecnología y proporcionó como resultado un claro beneficio económico al optar por el servicio almacenamiento en la nube de AWS S3.

The use of virtual assistants has become popularized by their adhesion by large companies. This is because they have brought benefits to a certain extent in people’s daily activities or in their tasks in organizations, with reminders, automation of repetitive tasks, proposing solutions for various situations, etc.. To meet this demand, several tools have been developed to create this type of agent. Following the same trends, this paper presents the Arisa Nest platform, under the Platform as a Service model, as a tool to create virtual assistants with resources to manage information about users, create algorithms to give more effective answers, consume web services to the interoperability with other systems and with proactivity behaviors. Finally, some cases are presented using the platform in real scenarios and tests.

The adoption of Hybrid cloud in enterprises has grown rapidly every year. Hybrid cloud solution implements the better of Private and Public cloud while proving that running of multiple application in different environment to provide segregation of personal useful data into a private environment from the raw meta data to reside on the cost-effective public cloud. This paper covers the deployment of hybrid cloud application with the use of Aneka PaaS (Platform as a Service) computing and Amazon Web Services Elastic Cloud Computing (AWS EC2) cloud service. The deployed application is automated using Orchestra- tor- an automation tool that allows EC2 resources to be deployed that enables uninterrupted running off application without having to take down the application during the situations of low compute resources. Aneka allows for the monitoring of resources to allow Optimization and billing for respective on demand resource usage. The proposed Orchestrator flow algorithm allows the application to load balance computing resources between the private and public cloud.

Актуальність. Дедалі більша кількість пристроїв отримує можливість підключення та управління через Інтернет, ще більшого зростання варто очікувати із широким впровадженням мобільних мереж п’ятого покоління. Також зростає і використання Хмарних обчислень, причому велика кількість компаній покладається на впровадженні Мульти-Хмар, використовуючи підхід платформа-як-сервіс для вирішення різноманітних задач. Пристрої ІР страждають від фрагментації, недостатньої сумісності між собою та сторонніми сервісами. Ці явища ускладнюють зростання даної сфери і не дають розкрити її повний потенціал при використанні великої кількості пристроїв. Мета і завдання дослідження. Метою магістерської роботи є забезпечення сумісної роботи великої кількості пристроїв ІР, покращення характеристик відмовостійкості та доступності даних в інфраструктурі Хмарних обчислень. Для досягнення мети було поставлено та вирішено наступні завдання: • дослідження Хмарної інфраструктури ІР; • визначення ключових характеристик для покращення; • розробка методу та його моделі; • огляд основних інструментів для реалізації; • розроблення стартап-проекту; Об’єкт дослідження – процес комунікації машина-людина та машина-машина в Хмарній інфраструктурі ІР. Предмет дослідження – методи організації Хмарної інфраструктури та передачі даних для забезпечення роботи ІР. Методи досліджень. В роботі було здійснено аналіз структури існуючих рішень та синтезовано нову структуру, що відповідає заданим критеріям. Наукова новизна одержаних результатів полягає у організації Хмарної інфраструктури ІР із виділенням нового проміжного рівня. Особистий внесок здобувача. Магістерська дисертація є самостійно виконаною роботою, що відображає набутий автором практичний досвід та отримані теоретичні і практичні результати щодо організації Хмарної інфраструктури ІР. Формулювання мети та завдань дослідження здійснювалось спільно із науковим керівником. Практична цінність. Отримані результати можуть бути застосовані як на практиці, для розвитку існуючих рішень, так і у майбутніх дослідженнях у сферах ІР, Розумних Міст, Підключеного Виробництва. Relevance. A growing number of devices get connected and controlled via the Internet, and even greater increase is forecasted with the global adoption of 5th generation mobile networks. Additionally, the global adoption of Cloud computing continues to grow as well. More companies shift towards the multi-Cloud approach, using a vast amount of PaaS to solve the different tasks required. IoT devices are highly fragmented and suffer from the lack of support between each other and 3rd party services. These problems negatively impact the industry’s ability to grow and stop it from unlocking IoT's full potential with billions of interconnected devices. The goal and tasks of the research. The master’s thesis aims to enable joint work of a great number of IoT devices and improve reliability and data availability in Cloud computing infrastructure. The following tasks were set and completed to reach the goal: • explore Cloud infrastructure for IoT; • determine key characteristics for improvement; • develop the method and its model; • overview key tools for implementing method; • making a startup project proposal; The object of study – M2H and M2M communication process in Cloud infrastructure for IoT. The subject of study – Cloud infrastructure organization and data transmission methods for supporting IoT applications. Research methods. An analysis of the structure of the existing methods was made, and a new design that meets the specified criteria was synthesized. The novelty of the result is Cloud IoT infrastructure's organization with the separation of the new intermediate layer. Personal contribution. Master’s thesis is an individually performed work reflecting the author’s practical experience and theoretical and practical results regarding Cloud IoT infrastructure organization. The definition of the goal and tasks of the research was carried out together with the scientific supervisor. Practical meaning. The work results can be practically used to improve existing solutions and inspire further research in the fields of IoT, Smart Cities, Connected Manufacturing, etc.

Azure Virtual Network (VNet) should be the starting point before deploying any virtual machine, web app, or storage container. The virtual networks in your Azure subscription are the global and local networks used for connectivity in the cloud. Internally, VNet allows secure private connections for all of your business resources. Externally, it supports exposing public endpoints to the Internet for access by clients or cyberattacks.

In Chapter 3, we covered all the platform as a service (PaaS) offerings. As you saw, there are many PaaS workloads in Azure, spanning different types of services. In future chapters, we explore data science and machine learning, specifically PaaS, network PaaS, and database PaaS. We repeat several times in this book that PaaS should be adopted whenever possible because of better service-level agreements, lower overhead in management, and ease in deployment.

There are many different facets and considerations for developing on Microsoft Azure. This chapter discusses why and how building for the cloud differs from on-premises development. It introduces you to different terms that will be explored further as we explain their blades in the Azure portal, including Function as a Service (FaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Then we will explain why you need a comprehensive strategy for debugging and profiling in Azure, as well as the basics of what that entails. Throughout the chapters, we will use a fictitious company to illustrate different scenarios, and in this chapter, we will introduce the company, its business objectives, and how we will use Azure to achieve them. The goal of this chapter is to provide a high-level view of development on Azure, as well as to set the tone of the book, in explaining an introductory perspective on debugging and profiling.

In this chapter, we provide a high-level view of the major services that are globally available, as well as elements of the Microsoft Azure platform as a service (PaaS). First, we discuss Azure Web Apps, support for developer frameworks, and apps that can run in containers. Next, you gain insight into database services and individual options for support. Then, we dive into Azure DNS as a powerful global service to support name resolution by customizing business service access. A companion service is Azure Traffic Manager, which supports DNS routing client requests based on profiles needed for business criteria.

Cloud computing has proven to be extremely useful in practice.Both the cloud-service-provider industry and its business, scientific, and consumer applications have grown exponentially. However, there are also benefits to be gained from viewing cloud computing abstractly: understanding its fundamental behaviors, but also its limitations. After a five-year run, this is the last issue of IEEE Cloud Computing. However, it is to be hoped that this final article contains the seeds of some ideas that others will find interesting and productive, so as to continue research to formalize such a theory. In effect, this article lays the foundation for future research by encapsulating much of this columns last five years into a comprehensive, integrated theoretical framework.

The paper presents an approach to the design and implementation of web-based environments for practical exercises in parallel and distributed computing (PDC). The presented approach introduces minimal development and operational costs by relying on Everest, a general-purpose platform for building computational web services. The flexibility of proposed service-oriented architecture enables the development of different types of services targeting various use cases and PDC topics. The generic execution services support the execution of different types of parallel and distributed programs on corresponding computing systems, while the assignment evaluation services implement the execution and evaluation of solutions to programming assignments. As was demonstrated by teaching two introductory PDC courses, the presented approach helps to enhance students’ practical experience while avoiding low-level interfaces, reducing the grading time and providing a level of automation necessary for scaling the course to a large number of students. In contrast to other efforts, the exploited Platform as a Service model provides the ability to quickly reuse this approach by other PDC educators without installation of the Everest platform.

This article presents the Templet Web cloud service. The service is designed for high-performance scientific computing automation. The use of high-performance technology is specifically required by new fields of computational science such as data mining, artificial intelligence, machine learning, and others. Cloud technologies provide a significant cost reduction for high-performance scientific applications. The main objectives to achieve this cost reduction in the Templet Web service design are: (a) the implementation of “on-demand” access; (b) source code deployment management; (c) high-performance computing programs development automation. The distinctive feature of the service is the approach mainly used in the field of volunteer computing, when a person who has access to a computer system delegates his access rights to the requesting user. We developed an access procedure, algorithms, and software for utilization of free computational resources of the academic cluster system in line with the methods of volunteer computing. The Templet Web service has been in operation for five years. It has been successfully used for conducting laboratory workshops and solving research problems, some of which are considered in this article. The article also provides an overview of research directions related to service development.

Cloud is referred to as a collection of infrastructure services, such as Infrastructure as a service (IaaS) and Platform as a service (PaaS), which are made available to us for utilization by various organizations in which the key factor is virtualization of data as it allow the user to manage, handle and compute a large number of tasks very easily. By referring to Cloud technologies we mean runtime such as Hadoop, Dryad and other Map Reduce frameworks. In this paper we would analyse the above mentioned software’s and techniques for the cloud system by comparing them on the basis of its processing speed, its data handling capacity, the nature of user friendliness. We would discuss large scale data analysis using different implementations on the above mentioned tools and after that we would give a performance analysis of these tools on the given implementation like Cap3, HEP, Cloudburst.

This century is described as Cloud Computing Technology over the Information Technology Business. Cloud computing advancement mostly relies on the Internet possesses the most advanced planning of computation. It records as piles of correlated and schmoozed hardware, software, and internet infrastructure. It blends grid computing and other computing as a community. A short overview of cloud computing is obtained from this research paper by consulting several papers on cloud technologies. The substance conveyed by this represents the perspective of shaping IT businesses and the idea of cloud computing.

As a core of the cloud computing, PaaS (Platform as a Service) provides a computing and software service platform to support large-scale cloud services. This paper presents one cloud comput...

Platform-as-a-Service (PaaS) Anbieter haben groses Interesse daran, durch das Angebot standardisierter Services das Entstehen dynamischer Okosysteme unabhangiger Softwareanbieter (Independent Software Vendor; ISV) zu fordern. Allerdings neigen Plattform-Okosysteme oft zu hoher Fluktuation und niedriger Loyalitat von ISVs. Eine bislang kaum erforschte Erklarung fur dieses Verhalten ist, dass aus Sicht der ISV Kosten entstehen, wenn sie einem Plattform-Okosystem beitreten und dort Apps entwickeln. Diese Kosten konnen hierbei schnell die durch das Okosystem generierten Mehrwerte uberwiegen. Dieser Beitrag untersucht, wie verschiedene Konfigurationen kostenverursachender Faktoren und ressourcenbasierter Nutzungspotentiale von Plattform-Okosystemen die Loyalitat eines ISV beeinflussen. Im Rahmen einer konfigurationstheoretischen Herangehensweise werden komplexe Interaktionseffekte von Kosten und Nutzen als kausale Bedingungen identifiziert. Basierend auf den Ergebnissen einer fuzzy-set Qualitative Comparative Analysis (fsQCA) extrahiert dieser Beitrag Muster, die die Loyalitat von ISV erklaren und die als Blueprint fur die Gestaltung von Plattform-Okosystemen dienen konnen.

If every programmer of cloud-hosted apps possessed exceptional technical capability and endless patience, the DevOps environment (also known as Platform-as-a-Service, or PaaS) would perhaps become irrelevant. However, the reality is almost always the opposite case. Hence, IT engineers dream of a reliable and usable DevOps environment that can substantially facilitate their developments and simplify their operations. Current DevOps environments include Google App Engine, Docker, Kubernetes, Mesos, and so forth. In other words, PaaS bridges the gap between vivid IT engineers and stiff cloud systems. In this paper, we comprehensively examine state-of-the-art PaaS solutions across various tiers of the cloud-computing DevOps stack. On this basis, we identify areas of consensus and diversity in their philosophies and methodologies. In addition, we explore cutting-edge solutions towards realizing a more fine-grained, full-stack DevOps environment. From this paper, readers are expected to quickly grasp the essence, current status, and future prospects of PaaS.

This paper presents Lysis, which is a cloud-based platform for the deployment of Internet of Things (IoT) applications. The major features that have been followed in its design are the following: each object is an autonomous social agent; the platform as a service (PaaS) model is fully exploited; reusability at different layers is considered; the data is under control of the users. The first feature has been introduced by adopting the social IoT concept, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability and information discovery efficiency. The major components of PaaS services are used for an easy management and development of applications by both users and programmers. The reusability allows the programmers to generate templates of objects and services available to the whole Lysis community. The data generated by the devices is stored at the object owners cloud spaces. This paper also presents a use-case that illustrates the implementation choices and the use of the Lysis features.

The availability of many open source systems offers affordable opportunities for organizations to build and adopt various types of cloud computing environments.

PaaSport is a cloud broker between PaaS providers and cloud application developers.PaaS offerings and application requirements are annotated with a PaaS OWL ontology.A semantic matchmaking and ranking algorithm recommends the best-matching offering.Cloud developers can deploy or migrate applications through the PaaSport broker.The algorithm scales linearly and is extensible. Platform as a service (PaaS) is one of the Cloud computing services that provide a computing platform in the Cloud, allowing customers to develop, run, and manage web applications without the complexity of building and maintaining the infrastructure. The primary disadvantage for an SME to enter the emerging PaaS market is the possibility of being locked into a certain platform, mostly provided by the market's giants. The PaaSport project focuses on facilitating SMEs to deploy business applications on the best-matching Cloud PaaS offering and to seamlessly migrate these applications on demand, via a thin, non-intrusive Cloud-broker, in the form of a Cloud PaaS Marketplace. PaaSport enables PaaS provider SMEs to roll out semantically interoperable PaaS offerings, by annotating them using a unified PaaS semantic model that has been defined as an OWL ontology. In this paper we focus on the recommendation algorithm that has been developed on top of the ontology, for providing the application developer with recommendations about the best-matching Cloud PaaS offering. The algorithm consists of: a) a matchmaking part, where the functional parameters of the application are taken into account to rule out inconsistent offerings, and b) a ranking part, where the non-functional parameters of the application are considered to score and rank offerings. źhe algorithm is extensively evaluated showing linear scalability to the number of offerings and application requirements. Furthermore, it is extensible upon future semantic model extensions, because it is agnostic to domain specific concepts and parameters, using SPARQL template queries.

Cloud computing has emerged as a new paradigm for on-demand access to a wast pool of computing resources that provides an alternative to using on-premises resources. This paper discusses the challenges related to using the cloud computing infrastructures for scientific computing. An approach based on Everest platform addressing these challenges is presented along with the prototype integration of Everest with Google Compute Engine. The proposed integration enables Everest users to seamlessly provision and use cloud-based computing resources for running different types of workloads including HPC and HTC applications. In contrast to other efforts, the presented approach also supports building and sharing domain-specific web services that automate execution of applications on dynamically provisioned cloud resources or hybrid infrastructures.