Cloud Architecture Research Articles

Towards hybrid-cloud infrastructure composition for SAP systems landscapes in smart manufacturing

To achieve the objective of smart manufacturing, the adoption of cloud computing is inevitable. However, the implications of fully or partially moving the manufacturing-related workloads to the cloud is a strategic decision that is rarely investigated thoroughly enough in the literature, especially for standard off-the-shelf enterprise IT applications, such as various SAP products. Specifically, in the related research, it is kept on a high level of conceptual representation without analysis of the specific cloud products required for such infrastructure. In this work, we discuss cloud setup for smart manufacturing while taking into account four main pillars of cloud infrastructure required for standard IT solutions, namely: compute, storage, networking, and backups. We also consider different degrees of high availability requirements since these fundamentally impact how cloud architecture should be designed. After discussing these elements, we demonstrate how these elements are mapped to the real-world cloud offerings on the example use case of Microsoft Azure. We close the paper with a discussion on further consideration of operational costs while pointing out the complexity of the pricing model.

THORIN: an Efficient Module for Federated Access and Threat Mitigation in Big Stream Cloud Architectures

In order to make cloud services attractive for several IT organizations, it is necessary to provide access control and to implement safe and reliable mechanisms of Identity and Access Management (IAM). In this work, we focus on security issues and challenges in the design and implementation of cloud architectures and, in particular, for the management of Big Stream applications in Internet of Things (IoT) scenarios. The proposed work introduces a new set of modules allowing a federated access control policy for cloud users. An analysis of possible threats and attacks against the proposed Big Stream platform is presented, investigating the system performance in terms of detection and elimination of malicious nodes. In particular, we propose a new module, denoted as Traffic Handler Orchestrator & Rapid Intervention (THORIN), which is very efficient in counteracting botnet-based threats.

Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system.

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10-20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5-15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10-12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8-11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance.

Secure and Scalable Architectures for Cloud Computing

Cloud computing has emerged as a cornerstone of modern computing infrastructure, offering unparalleled scalability and flexibility for diverse applications. However, the widespread adoption of cloud services has also raised significant concerns regarding security and scalability. As organizations increasingly rely on cloud environments to store and process sensitive data, ensuring robust security measures while maintaining scalability becomes paramount. This paper presents a comprehensive examination of secure and scalable architectures for cloud computing, addressing the challenges and proposing innovative solutions to enhance the resilience and efficiency of cloud infrastructures. The first section of the paper delves into the fundamental principles of cloud computing, outlining its key characteristics and architectural components. By understanding the underlying architecture of cloud systems, stakeholders can better appreciate the intricate interplay between security and scalability. Subsequently, the paper elucidates the multifaceted security threats facing cloud environments, ranging from data breaches and malicious attacks to insider threats and compliance issues. These challenges underscore the critical importance of implementing robust security mechanisms to safeguard sensitive data and infrastructure resources. In response to these challenges, the paper presents a systematic analysis of existing security mechanisms and architectural paradigms designed to fortify cloud environments. This includes encryption techniques, access control mechanisms, authentication protocols, and intrusion detection systems, among others. Furthermore, the paper explores the concept of defense-in-depth strategies, emphasizing the importance of layering security measures to mitigate the impact of potential breaches and vulnerabilities. In tandem with security considerations, the paper examines strategies for achieving scalability in cloud architectures. Scalability is a defining characteristic of cloud computing, enabling on-demand resource allocation and elastic scaling to accommodate fluctuating workloads. However, achieving scalability while ensuring security presents unique challenges, particularly in multi-tenant environments where resources are shared among disparate users. To address these challenges, the paper explores architectural frameworks such as microservices, containerization, and serverless computing, which offer inherent scalability benefits while facilitating robust isolation and security. Moreover, the paper investigates the role of automation and orchestration in optimizing cloud scalability and security. Through the use of DevOps practices and infrastructure- as-code tools, organizations can streamline deployment processes, enforce security policies, and dynamically scale resources in response to evolving demands. Additionally, the paper examines emerging technologies such as edge computing and federated architectures, which extend the scalability and security benefits of cloud computing to edge devices and distributed environments. In conclusion, this paper underscores the intrinsic link between security and scalability in cloud computing architectures. By adopting a holistic approach that integrates robust security measures with scalable design principles, organizations can mitigate risks, enhance operational efficiency, and unlock the full potential of cloud computing. As the landscape of cloud technologies continues to evolve, ongoing research and innovation in secure and scalable architectures will remain essential to address emerging threats and optimize performance in cloud environments.

Towards a container-based architecture for CMS data acquisition

The CMS data acquisition (DAQ) is implemented as a service-oriented architecture where DAQ applications, as well as general applications such as monitoring and error reporting, are run as self-contained services. The task of deployment and operation of services is achieved by using several heterogeneous facilities, custom configuration data and scripts in several languages. In this work, we restructure the existing system into a homogeneous, scalable cloud architecture adopting a uniform paradigm, where all applications are orchestrated in a uniform environment with standardized facilities. In this new paradigm DAQ applications are organized as groups of containers and the required software is packaged into container images. Automation of all aspects of coordinating and managing containers is provided by the Kubernetes environment, where a set of physical and virtual machines is unified in a single pool of compute resources. We demonstrate that a container-based cloud architecture provides an acrossthe-board solution that can be applied for DAQ in CMS. We show strengths and advantages of running DAQ applications in a container infrastructure as compared to a traditional application model.

Merging control-flow and dataflow architectures on a single chip

Computing power rises predominantly by increasing the number of cores in modern processors and the number of processors in cluster and cloud architectures. Along with increasing processing power, high-performance computing requirements also rise. The majority of the computing infrastructure includes control-flow processors that are based on the von Neumann paradigm. On the contrary, the principle of dataflow architectures is based on the data flowing through the already configured hardware. Recent research has proposed hybrid architectures, where both control-flow and dataflow hardware would exist on the same chip die. This article proposes a new hybrid control-flow and dataflow architecture where the control-flow hardware resembles modern graphical cards with thousands of cores and each GPU core has a reasonable amount of data-flow hardware. In this way, the advantages of dataflow architecture are exploited, including faster processing of high-performance computing algorithms and lower power consumption, while the conventional problem of communicating between control-flow and dataflow architectures is minimized. The proposed architecture is tested by analyzing the conjugate gradient method executed on both control-flow and dataflow hardware. The execution of the algorithm is divided onto GPU cores, and the execution of repeated instructions on each GPU core is delegated to the assigned dataflow hardware. The results indicate that it is possible to accelerate the execution of algorithms using the proposed architecture.

Securing Cloud Environment Using Hybrid (Adaptive Public Key) Model

Although data security has always been important, it is now even more so given the significance of cloud computing and the enormous amounts of complex data it holds. In cloud computing, having data accessible at all times is vital, as is collecting and preserving data without loss or corruption. The use of appropriate algorithms is one of the most important aspects of security. These protocols protect data by limiting unauthorized access. In order to ensure the security of your data, you need to conduct research on the security risks associated with public clouds. The purpose of this document is to provide an in-depth review of public clouds and the security issues associated with them. In order to review the literature review that provides an in-depth understanding of cloud storage and security issues, it is necessary to overcome several obstacles. As a result, based on an analysis of cloud architecture, we provide the data security model that can be applied to cloud computing. Our cloud computing data security model is now better. Lastly, the work can be done in a direction that benefits from faster encryption times and increased data secrecy by utilising machine learning and authentication technologies for safe cloud storage.

Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing

Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimization are included in the current paper. A thorough analysis of the data generated by this activity is essential to resolving the resource allocation mechanism of the cloud architecture. To fully utilize virtual machines with a similar weight distribution, a strategy-oriented mixed support and load balancing structure has been developed in this work. To minimize make-span time and accomplish initial load balancing, the SPSO-TCS technique combines Time-Conscious Scheduling with Supportive Particle Swarm Optimization. Finding the optimal make span time minimization for each virtual environment is the aim of this stage. Its main objective is to discover the sequence of activities with the least computation time and to reduce the time required to finish each operation. Utilizing the hybrid idea leads to a decrease in makespan and the use of the least amount of energy.

Research on the Innovative Model of Artificial Intelligence Technology Facilitating Sports Management in Colleges and Universities

Abstract The challenge of managing information in collegiate physical education has progressively escalated, thereby amplifying the complexities inherent in the sports management domains at higher education institutions. The advent of artificial intelligence technology heralds a promising avenue for the evolution of sports management frameworks within these settings. This study delineates the development of a specialized cloud platform tailored for the management of collegiate sports, encompassing aspects such as teaching and research management, quality assurance in education, general teaching affairs, practical instruction, and support for auxiliary teaching processes. This platform is engineered using the OpenStack cloud architecture, and it meticulously explores advanced scheduling algorithms for virtual machines to enhance platform efficiency. Systematic testing of the platform reveals critical performance metrics; notably, prior to optimization, the CPU utilization of the educational system spikes to 100% with a Time to First Byte (TTFB) reaching indefinite timeouts when concurrent accesses surpass 9, 000, leading to a system shutdown and cessation of service availability. Post-optimization, the platform robustly sustains business accessibility even under similar stress conditions. Furthermore, the empirical usage data of the sports management systems indicate predominant utilization for fulfilling work-related needs (0.796), psychological requirements (0.74), and the pursuit of enhanced operational efficiency (0.732).

Availability and Reliability Modeling of Mobile Cloud Architectures

In recent years, mobile cloud computing has attracted researchers and industries' interest since it maximizes gains with mobile computing in different areas, such as healthcare, sports, and business. However, the inherent challenges associated with mobile computing (e.g., battery provisioning and wireless network instability) may limit mobile cloud achievements. This paper provides an availability and reliability study of mobile clouds and evaluates distinct architectures and strategies. Our evaluation is based on hierarchical heterogeneous models and focuses on steady-state availability, reliability, annual downtime, and costs of implementation and provisioning of the cloud infrastructure. The results point out that service downtime may be significantly decreased by adopting actions such as spare batteries for mobile devices and the adoption of distinct redundancy strategies. We also propose a way to support decision-making considering private and public clouds by comparing some architectural choices while considering factors such as budget expenses for hardware purchases and power consumption. After 36 months, the private cloud has a cumulative cost that is smaller than the cost of a public cloud service.

Leveraging hybrid cloud architectures and Cosmos DB for sustainable IT solutions in ecology and natural resource management

Leveraging hybrid cloud architectures and Cosmos DB for sustainable IT solutions in ecology and natural resource management

Internet of things and cloud based smart parking system design criteria

In urban areas, traffic density is seen due to the congested residential areas and the high number of vehicles. The problem of drivers searching for parking spaces in central areas creates traffic. The Internet of Things (IoT) technology offers important solutions with its networking feature to solve problems such as traffic congestion, road safety and inefficient use of parking areas, which are waiting for solutions within the scope of Intelligent Transportation Systems. In this study, the technological infrastructures used by IoT-based smart parking systems are examined and integrated building models are proposed for system designs where parking lots are managed. For smart park design, devices, networks and cloud architecture used in IoT-based systems were examined and requirements were determined. The criteria of an application based on design central management are given. The criteria of an application based on design center management are given. Thanks to the smart parking system to be created in the light of these criteria, the closest parking area will be determined. These designed IoT-based systems will contribute to the reduction of traffic congestion, loss of time in full parking areas, air pollution caused by stop and start vehicles, and fuel savings in the economic field.

Developing a Centralized Receipt Service to Streamline Restaurant Operations and Reduce Costs

The fast food industry, which the restaurant industry falls under, has several challenges, including receipt management. Conventional document management means are ineffective, expensive, and inconducive to the best performance of restaurants and the satisfaction of consumers. This article discusses the challenges arising from the decentralized receipt service and how they have developed and deployed the Centralised Receipt Service to solve these issues with modern technologies like Go Gin framework and AWS lambda. The service thus factors in cloud architecture to improve the receipt data storage, retrievals, management, and less paper use. It also improves customer satisfaction through digital receipts through Short Messaging Service and mobile responsive web front end. Receiving centralized services helps enhance performance by cutting operational costs, reducing paperwork, and increasing record-keeping. Besides, it helps the business reduce paper usage, positively contributing to environmental conservation. It provides a potential growing scale for all forms of businesses; restaurants can use it to address expanding customers’ demands for digital services. It also exposes future possibilities for using AI for predictive analysis and customized client communications. Therefore, by consolidating restaurant receipt management, they can enhance customer satisfaction with less spending and set the business up for success in the dynamic digital space. This study supports that this centralized method for receipt services is feasible and fundamental in today’s restaurant businesses since it comes with a package of effectiveness, sustainability, and customer appeal opportunities.

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

RESEARCH AND DESIGN OF THE MARKETING CLOUD SALESFORCE CRM ARCHITECTURE DEPENDING ON THE COMPANY'S MARKETING STRATEGY

The purpose of the study is to increase the level of effective attraction of new customers and retention of the company's existing customer base through the implementation of innovative marketing strategies. The expediency of using the concept of customer relationship management (CRM) and the use of CRM-class information systems to achieve the goal has been proven. The CRM platform Salesforce Marketing Cloud, which belongs to cloud types of information systems, was studied. A description of the Salesforce Marketing Cloud business logic is given, which includes decision-making based on analytical data, customer segmentation, automation of an individual approach to customers with the aim of increasing customer loyalty and increasing company revenue. Salesforce Marketing Cloud provides an opportunity to create and manage personalized marketing activities, interact with customers through various channels, analyze the effects of marketing efforts for continuous improvement of the company's marketing strategies. The functionality and features of the main components of Salesforce Marketing Cloud were studied. Salesforce Marketing Cloud allows you to effectively manage email marketing, mobile marketing, social media campaigns and other types of marketing. Salesforce Marketing Cloud empowers companies to create and manage personalized marketing efforts, engage with customers across multiple channels, and analyze their marketing efforts for continuous improvement. Salesforce Marketing Cloud architecture was designed for behavioral segmentation of the company's customers. A web application has been developed to improve the platform users' experience with customer data stored in Data Extensions. To automate customer segmentation based on their behavioral data, Automation Studio was used, SQL queries were built, and Data Views system data tables were used. The developed architecture of Marketing Cloud Salesforce CRM based on behavioral segmentation is an important tool for the implementation of the company's marketing strategy, ensuring the successful attraction and retention of customers, as well as for achieving strategic goals.

ОБЛАЧНЫЕ СЕРВИСЫ ВЕДЕНИЯ БУХГАЛТЕРСКОГО УЧЕТА: ДОСТОИНСТВА И НЕДОСТАТКИ

The subject of this research is the accountancy concept of applying cloud technologies (cloud services) that would provide hardware-independent access to the functions of typical accountancy apps. The authors analyze merits and flaws of organizing ‘cloud accountancy’. They also give consideration to the problems of paramount importance faced by both end customers and those companies developing software of the said type. The article presents its author’s view of practicability and prospects of employing accounting automated systems of cloud architecture under present-day conditions with consideration to the Russian economy realia

Scaling Digital Biomarker Discovery with the Cloud and Wearable Devices

AbstractBackgroundIn clinical trials and other disease research, biomarkers provide a valuable indication of drug performance and disease progression. Traditionally, these biomarkers may include genomic factors, lab test results, and more, but we now can easily expand into longitudinally collected digital biomarkers from consumer smart devices.MethodThe Amissa platform, built on the Microsoft Azure cloud, enables the collection of high frequency data from consumer smartwatches (such as the Apple Watch and Samsung Galaxy watch devices). The API‐driven cloud architecture enables secure sensor data collection including location, gyrometry/accelerometry, activity monitoring/pedometry, and biosensors such as oxygen saturation and heart rate. This solution allows for sub‐minute capture frequency while balancing device battery drain. The ISO 27001:2013‐compliant platform is globally scalable through an innovative cloud‐native design that ensures security and compliance.ResultCaregivers, researchers, and healthcare providers can now monitor loved ones/patients through consumer‐grade wearables, providing a cost‐effective, accurate, and flexible solution for biomarker discovery, data collection, and remote monitoring.ConclusionThe Amissa platform successfully enables digital biomarker development and helps to address disparities in clinical trials and research studies of underrepresented groups through decentralized data collection. Plus, the platform address security and privacy considerations while enables collaborative research participation and flexible study designs. Note: In this presentation, we will discuss the NIH‐funded work of Amissa and the cloud‐first approach taken to build the world’s first global‐scale platform for disease research using wearable devices. We will cover technical design patterns, considerations for privacy and security, the effect of decentralized data collection on addressing health disparities and inclusion in clinical trials, and the future of digital biomarker discovery in Alzheimer’s disease and related dementias.

Efficient Data Management Strategies for Cloud Computing Environments

Abstract: Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as One drive, iCloud and Google One) and allows them to cut costs on hardware and IT support. Variety of data to be analysed continues to quickly rise as new applications like social network analysis, semantic Web analysis, and bioinformatics network analysis proliferate. The problem of efficiently managing and analysing massive amounts of data is both fascinating and important. There has been a lot of focus on big data lately, from researchers to business leaders to policymakers. From both a technical and practical perspective, this paper presents numerous methods for processing massive amounts of data. We begin by discussing the most important aspects of big data analytics and processing from the perspective of cloud data management and the mechanisms involved in processing large amounts of data. This covers the cloud computing platform, cloud architecture, cloud database, and data storage scheme. It examines the responsibilities of hosting businesses who own and run cloud computing datacentres (like Amazon) with regards to data security and privacy. As an example, it takes into account the fact that many cloud service providers (like Dropbox and Salesforce) really rent out space in the cloud' from larger hosting corporations. And it examines the business and private ‘clouders’ using these services. As we all familiar with cloud computing, it’s not a latest technology, rather we can mention it as an emerging technology where most of the industry is trying to store not only its crucial data for redundancy but also looking for the service management. In that scenario first thing comes in mind is management of data in most efficient way possible.

Cloud-based Data Analytics for Business Intelligence

Abstract: In the era of data-driven decision-making, organizations are turning to cloud-based data analytics for business intelligence to overcome the limitations of traditional on-premises systems. This paradigm shift offers the promise of scalable, agile, and advanced analytics capabilities. This paper explores the landscape of cloud- based data analytics for business intelligence by investigating existing systems, challenges, and opportunities. The study first examines leading cloud platforms such as Amazon Web Services (AWS) Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and Snowflake[11]. It evaluates their features, scalability, and integration options to assess their suitability for modern BI needs. Moreover, the research identifies critical challenges in transitioning to cloud-based analytics, including data integration complexities, security concerns, and cost management. The integration of advanced analytics techniques, such as machine learning and AI, into cloud- based environments is also explored. The study delves into the benefits and challenges of predictive analytics, anomaly detection, and other emerging capabilities that empower organizations to extract deeper insights from data. Furthermore, hybrid cloud architectures, which combine on-premises infrastructure with cloud resources, are investigated. Strategies for seamless data integration and workload distribution are discussed, enabling organizations to strike a balance between performance and data governance.

Examines the value of cloud computing adoption as a proxy for IT flexibility and effectiveness

PurposeThe purpose of this study is to investigate the value of cloud computing adoption (CCA) as a proxy for information technology (IT) flexibility (ITF) and IT effectiveness (ITE) among Small- and medium-sized enterprises (SMEs) in Jordan.Design/methodology/approachA research framework with five hypotheses has been developed based on the results of previous studies. Partial least squares-structural equation modeling has been used for data analysis.FindingsThe results revealed that elements of ITF in two domains, IT technical resource (connectivity, modularityand compatibility) and IT human resource (IT personnel skills) specific to CCA, were significantly correlated with ITE.Research limitations/implicationsThe findings have crucial implications: they contribute to the research community, administrators and cloud computing providers (CSPs) concerning the framework-improved procedure for CCA. The proposed model can enhance the awareness of service providers about why some SMEs accept cloud computing services, whereas actually the same ones having the same type of business do not. In addition, the above providers should enhance their interaction with the SMEs that contributed to the cloud computing knowledge to make a well-organized setting for the CCA, specifically, SMEs need that adopt an on-premise private cloud architecture. Moreover, necessary to determine the challenges in deploying solutions from the perspective of CSPs. The sample has been limited to Jordan respondents.Practical implicationsThe research studies about the usage of cloud computing have shown its effects on SMEs today. Also, the different impacts of cloud computing on other sectors are at the center of attention. SMEs could get significant advantages by carefully considering and managing CCA from the ITF and ITE perspective.Originality/valueTo the best of the author’s knowledge, this is the first study to develop a framework for CCA based on the standpoint of ITF in two domains, IT technical resources (connectivity, modularity and compatibility) and IT human resources (IT personnel skills) and effectiveness.