Hybrid Cloud Research Articles

Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment

The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.

Smart technologies in banking

The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization

The widespread adoption of public and hybrid clouds, along with elastic resources and various automation tools for dynamic deployment, has accelerated the rapid provisioning of compute resources as needed. Despite these advancements, numerous resources persist unnecessarily due to factors such as poor digital hygiene, risk aversion, or the absence of effective tools, resulting in substantial costs and energy consumption. Existing threshold-based techniques prove inadequate in effectively addressing this challenge. To address this issue, we propose an unsupervised machine learning framework to automatically identify resources that can be de-provisioned completely or summoned on a schedule. Application of this approach to enterprise data has yielded promising initial results, facilitating the segregation of productive workloads with recurring demands from non-productive ones.

Abstract 3558: An integrated data platform to support the international alliance for cancer early detection

Abstract Cancer early detection is one of the most critical areas of cancer research, as it offers the greatest potential for improving patient outcomes. The International Alliance for Cancer Early Detection (ACED) is a global partnership of world-leading cancer research institutions in the UK and US, established in 2019 to accelerate and revolutionize research in this field. ACED brings together the expertise of the Canary Center at Stanford University, the University of Cambridge, the Knight Cancer Institute at Oregon Health and Sciences University, University College London, and the University of Manchester, together with Cancer Research UK, with the goal of catalyzing new collaborations and research in the field of early detection. One of the major challenges to enabling international collaborations across different institutions is the ability to collect, share and discover datasets between researchers and institutions. To enable collaboration on innovative data science, fundamental functions include, 1) controlled data sharing mechanisms; 2) structured metadata to enable data exploration and data discovery; and 3) enabling easy computational access to that data. To advance researchers' collaboration, ACED began development of an Integrated Data Platform (IDP). The ACED-IDP is based on the Gen3 software platform developed by the University of Chicago's Center for Translational Data Science. Based on software systems originally developed for the NCI's Genomic Data Commons, Gen3 has been used in several other data projects including The Blood Profiling Atlas in Cancer (BloodPAC), Australian BioCommons and numerous other research data platforms. The unique nature of ACED required unique innovations to be made for the development of the IDP. Each of the member institutions within the alliance has different existing computer infrastructures, separate authentication platforms and heterogeneous data types forming the basis of their research. The IDP sought a cloud-ready strategy, while still being cognizant of the extreme costs associated with cloud egress fees that hamper researchers' ability to download data. Additions were made to the Gen3 platform to allow for hybrid cloud support, allowing on-premises as well as cloud object storage systems to be linked to the platform. This innovation permits each institution to share files using the mechanisms that they see fit. To unify the various datasets, the standard Gen3 schema was replaced with one derived from the Fast Healthcare Interoperability Resources (FHIR) standard. To support import from clinical data sets, tooling to enable import and export Observational Medical Outcomes Partnership (OMOP) data has been integrated. With this platform in place, we hope to advance international collaborations and accelerate early cancer detection research. Citation Format: Brian Walsh, Liam Beckman, JD Burchett, Matthew Peterkort, Jordan Lee, Michael Fitzsimons, Peter Vassilatos, Binam Bajracharya, Craig Barnes, Jawad Qureshi, Robert Grossman, Carrie Yakura, Yaozhi Lu, Sarah Burge, Daniel Kelberman, Erin Watson, Kyle Ellrott. An integrated data platform to support the international alliance for cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3558.

Standardized Telemedicine Software Development Kit with Hybrid Cloud Support

In modern Web development, it is expected that systems operating in the same area can be easily integrated. For common integration points, it is recommended to use a standardized data model and a common interface during the development as this will facilitate further integrations. The use of the cloud infrastructure is increasingly popular in telemedicine, but taking into account the goals, the productivity of the development, the availability of the system and the various regulations, choosing the right solution is not trivial. Inclouded platform consists of numerous currently active telemedical microservices that are working with a common software development kit. This tool provides a standardized data model for document-oriented database systems, has support for public and private clouds by using the classic Data Access Object (DAO) analogy and contains a lot of convenient functions as well. Furthermore, it is found that our solutions can significantly increase development productivity and is confirmed by measurements taken which involved software developers.

Enhancing Energy Efficiency in Telehealth Internet of Things Systems Through Fog and Cloud Computing Integration: Simulation Study.

The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency. This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems. The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency. Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings. The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.

Exploring Strategic Decision-Making Process In Hybrid Cloud Computing Adoption Among Small And Medium-Sized Enterprises

This study delves into the strategic decision-making process concerning the adoption of hybrid cloud computing among Small and Medium-Sized Enterprises (SMEs) within the context of Bahrain. With the proliferation of digital transformation and the evolving landscape of technological infrastructure, the adoption of hybrid cloud solutions presents a compelling avenue for SMEs to enhance operational efficiency and competitive advantage. This research employs a qualitative approach, engaging in-depth interviews with 11 key stakeholders from diverse SMEs across Bahrain. Through thematic analysis, this study elucidates the multifaceted dimensions of the strategic decision-making process, encompassing factors influencing adoption, perceived benefits, challenges encountered, and the integration of hybrid cloud computing within the broader organizational strategy. The findings shed light on the considerations, including technological, organizational, regulatory, and socio-cultural aspects, shaping the adoption landscape of hybrid cloud solutions among SMEs in Bahrain.

Multi-Cloud Deployments and Hybrid Cloud Architecture

Multi-Cloud Deployments and Hybrid Cloud Architecture

THE STRUCTURE OF INFORMATION AND EDUCATION ENVIRONMENT OF A MEDICAL UNIVERSITY

The authors examine the process of transformation of the educational infrastructure of medical universities during the transition to a mixed form of studying while COVID – 19 quarantine. Analysis of scientific publications on the digital transformation of the higher education system has shown the effectiveness of a hybrid model for organizing a virtual learning environment, which combines the ground-based infrastructure of the university and can include private and public cloud services. Hybrid model allows providing appropriate technological conditions for quality organization of teachers' work in online mode of distance learning system. Cloud services allow organizing a virtual group educational environment - a virtual classroom and a personal environment for students. The authors analyzed the changes in the infrastructure of Zaporizhzhia State Medical University in the process of digital transformation of the educational process during the period of COVID-19 quarantine, which became the ground for the development of a structural model of the information and educational environment of the university. The work focuses on the fact that one of the features of the educational process in medical universities is the usage of special learning tools: software and hardware systems for examination of a patient's condition, technical means of telemedicine, classroom self-training sessions for Licensing Integrated Exams “KROK” 1,2,3 on the basis of licensed software, etc. The authors conducted a systematic analysis and identified the technological components of the unified information educational environment of medical HEIs, namely: corporate computer network; workplaces of participants in the development of online courses (designer, lecturer of videoconferencing, moderator of video lectures or webinars, designer of video processing; specialized modeling systems; simulation complexes; software and hardware complexes for processing biological signals; specialized classrooms: computer room of the library, lecture halls for remote video lectures with interactive communication, lecture halls for interactive lectures, media centers for webinars and telemedicine consultations; cloud services . The proposed structure of information and educational environment of a medical university was implemented in the educational process of Zaporizhzhia State Medical University which made possible the creation of a cloud-oriented educational environment. The usage of hybrid cloud model allowed to solve the issue of integration of virtual environments of departments, in particular, territorially remote ones, and the ground infrastructure of the university.

Evaluation of distributed data processing frameworks in hybrid clouds

Distributed data processing frameworks (e.g., Hadoop, Spark, and Flink) are widely used to distribute data among computing nodes of a cloud. Recently, there have been increasing efforts aimed at evaluating the performance of distributed data processing frameworks hosted in private and public clouds. However, there is a paucity of research on evaluating the performance of these frameworks hosted in a hybrid cloud, which is an emerging cloud model that integrates private and public clouds to use the best of both worlds. Therefore, in this paper, we evaluate the performance of Hadoop, Spark, and Flink in a hybrid cloud in terms of execution time, resource utilization, horizontal scalability, vertical scalability, and cost. For this study, our hybrid cloud consists of OpenStack (private cloud) and MS Azure (public cloud). We use both batch and iterative workloads for the evaluation. Our results show that in a hybrid cloud (i) the execution time increases as more nodes are borrowed by the private cloud from the public cloud, (ii) Flink outperforms Spark, which in turn outperforms Hadoop in terms of execution time, (iii) Hadoop transfers the largest amount of data among the nodes during the workload execution while Spark transfers the least amount of data, (iv) all three frameworks horizontally scale better as compared to vertical scaling, and (v) Spark is found to be least expensive in terms of $ cost for data processing while Hadoop is found the most expensive.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

A multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning in hybrid cloud manufacturing

Cloud manufacturing (CMfg) is a new network manufacturing mode, where one of the essential issues is service composition and optimal selection (SCOS), attracting a wide range of scholars' attention and research. At present, scholars at home and abroad mainly focus on studying the SCOS model in a single type of cloud environment, which integrates and optimizes large group enterprises' internal resources and capabilities. However, since the characteristics and objectives of the SCOS problem are different in different cloud environments (private, public, and hybrid cloud), there are many dynamic switching scenarios of manufacturing resources in different cloud environments. At this time, SCOS models in a single type of cloud environment are no longer applicable. Thus, how to realize the maximization of multi-stakeholder benefits through the adaptive partitioning of manufacturing resources in different cloud environments is an urgent problem to be solved. To fill the gap, a multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning (SCOS-MUB-ARP) is proposed for quickly obtaining the optimal service composition that maximizes the benefits of multi-stakeholder in hybrid cloud manufacturing. To address the SCOS-MUB-ARP, a multi-objective optimization algorithm based on a combination of the NSGA-II algorithm and the mayfly algorithm (MMA-NSGA2) with several optimization strategies is presented, which contains several problem-specific optimization strategies. Numerical experiments and application cases show the effectiveness of the proposed MMA-NSGA2 in optimizing and balancing multi-objectives, compared with some well-known algorithms.

Maximizing the Efficiency and Performance: The Role of Load Balancer in Modern Cloud Computing Environment

Load balancing is a process of distributing the client requests from client application to the backend servers in an efficient way throughout the internet.There are several algorithms available to distribute this request. Cloud computing generally based on “pay per use” model that provides services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS). we have different types of cloud types like Public, Private, Hybrid and Community clouds. While making the efficiency and performance maximum we will face some challenges and we need to make key strategies, The evolving role it plays in the pursuit of superior performance and scalability. Optimizing resource utilization and maintaining consistent performance are crucial for businesses and organizations in the dynamic cloud computing landscape. In this we have discussed about Golden Eagle optimization Algorithm (GEO) and Mouse And Cat optimization(CMO) algorithm .The proposed Mouse-Cat-Golden Eagle (MCGEO) algorithm employs a unique combination of three animal- inspired optimization techniques to dynamically allocate tasks across servers in a distributed computing environment.

Retracted: An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

Retracted: An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

Notice of Violation of IEEE Publication Principles: Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds

Notice of Violation of IEEE Publication Principles “Scientific Workflow Partitioning and Data Flow Optimization in Hybrid Clouds” by Rubing Duan, Rick Siow Mong Goh, Zheng Qin and Young Liu in Pre-Prints for IEEE Transactions on Cloud Computing After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and it should not be used for research or citation The scheduling and execution of workflow applications are complex issues because of the dynamic and heterogeneous nature of distributed computing environments like hybrid clouds. While hybrid cloud computing environments provide good potential for achieving high performance and low economic cost, it also introduces a broad set of unpredictable overheads. This paper describes a novel approach to refine workflow structure and optimize intermediate data transfers without changing the scale of scheduled cloud service for large-scale scientific workflows containing thousands (or even millions) of tasks. These methods include pre- and post-partitioning of workflows, data flow optimization, static and dynamic optimization, as well as virtual single execution environment incorporated into a workflow management system.We demonstrate the effectiveness of our methods on the three real applications, Montage, Broadband, and WIEN2k, executed in a real hybrid cloud.

Hybrid Cloud Computing: An In-Depth Analysis of Integration Strategies, Characteristics, and Prospective Future Applications

Hybrid cloud computing plays an important role of developing landscape of IT infrastructure. It integrates the control and security inherent in private clouds with the scalability and resource availability characteristic of public clouds. This paper provides a thorough exploration of the characteristics involved in combining these cloud models and highlights essential aspects such as flexibility, scalability, cost savings, and security. Based on a thorough literature review along with case studies, the study explores how current technological advancements, including containerization, and artificial intelligence, are influencing the development of hybrid cloud infrastructures. The discussions show that effective integration strategies not only increase operational efficiency but also introduce substantial benefits to diverse fields including finance, healthcare, and education. This article provides a comprehensive understanding of hybrid cloud and builds up a foundation and direction for future studies focused on optimizing cloud integration and application.

Innovative Hybrid Cloud Solutions for Physical Medicine and Telerehabilitation Research.

The primary objective of this study was to develop and implement a Hybrid Cloud Environment for Telerehabilitation (HCET) to enhance patient care and research in the Physical Medicine and Rehabilitation (PM&R) domain. This environment aims to integrate advanced information and communication technologies to support both traditional in-person therapy and digital health solutions. Telerehabilitation is emerging as a core component of modern healthcare, especially within the PM&R field. By applying digital health technologies, telerehabilitation provides continuous, comprehensive support for patient rehabilitation, bridging the gap between traditional therapy, and remote healthcare delivery. This study focuses on the design, and implementation of a hybrid HCET system tailored for the PM&R domain. The study involved the development of a comprehensive architectural and structural organization for the HCET, including a three-layer model (infrastructure, platform, service layers). Core components of the HCET were designed and implemented, such as the Hospital Information System (HIS) for PM&R, the MedRehabBot system, and the MedLocalGPT project. These components were integrated using advanced technologies like large language models (LLMs), word embeddings, and ontology-related approaches, along with APIs for enhanced functionality and interaction. The HCET system was successfully implemented and is operational, providing a robust platform for telerehabilitation. Key features include the MVP of the HIS for PM&R, supporting patient profile management, and rehabilitation goal tracking; the MedRehabBot and WhiteBookBot systems; and the MedLocalGPT project, which offers sophisticated querying capabilities, and access to extensive domain-specific knowledge. The system supports both Ukrainian and English languages, ensuring broad accessibility and usability. The practical implementation, and operation of the HCET system demonstrate its potential to transform telerehabilitation within the PM&R domain. By integrating advanced technologies, and providing comprehensive digital health solutions, the HCET enhances patient care, supports ongoing rehabilitation, and facilitates advanced research. Future work will focus on optimizing services and expanding language support to further improve the system's functionality and impact.

Special features of service design in microservices architecture in cloud computing with container virtualization

The article addresses the issue of service design in microservices architecture in cloud computing under conditions of container virtualization. Service design is the process of modeling a service to facilitate interaction between the provider and users. The foundation of service design lies in understanding user behavior, their needs, and motivations to create a service that is convenient and memorable, increasing the chances of reuse. Microservices architecture technology is an approach that involves breaking down applications into smaller and more manageable blocks (microservices), each performing a specific function. This approach differs from monolithic programs where all components are tightly interconnected and reside in a single codebase. The new approach opens up unique possibilities for utilizing various technologies, including containerization, orchestration, hybrid cloud, and more, offering excellent opportunities for optimizing applications through microservices. To address the identified challenges in the article: a) it examines how the new approach introduces complexities, such as the lack of proper monitoring and event logging; the possibility of incorrect service detailing; complexities in data management and synchronization; weaknesses in communication and automation processes; manifestations of bottlenecks in terms of productivity and scalability, among others; b) it is demonstrated that all these complexities can be resolved through effective service design in microservices architecture. This means that when designing microservices architecture in cloud computing with container virtualization, the challenge of effective service design is addressed by creating a balance between the detailing of services and their functionality to achieve maximum productivity and system functionality. Solving this task is possible primarily through an understanding of the principles of construction and application of microservices in cloud computing under conditions of container virtualization.

Multi-Mode Instance-Intensive Workflow Task Batch Scheduling in Containerized Hybrid Cloud

Multi-Mode Instance-Intensive Workflow Task Batch Scheduling in Containerized Hybrid Cloud

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