Multi-cloud Platform Research Articles

Adaptive Security Paradigms: The Role of Al in Safeguarding Distributed Data Across Multi-cloud Platforms

The proliferation of multi-cloud infrastructures in modern data management strategies has introduced complex security challenges that traditional measures struggle to address effectively. This article investigates the potential of AI-powered security frameworks to enhance distributed data protection across diverse cloud environments. By leveraging advanced machine learning algorithms and predictive analytics, these frameworks offer real-time threat detection, adaptive access controls, and intelligent encryption management. The article examines the key components of AI-driven security systems, including automated anomaly detection and behavior analysis, and their integration with existing security protocols. Through a series of case studies and real-world applications, we demonstrate the efficacy of these frameworks in identifying vulnerabilities, initiating proactive security measures, and maintaining compliance with industry regulations. Our findings indicate that AI-powered security frameworks provide a scalable, adaptive, and robust solution for safeguarding distributed data assets in the dynamic landscape of multi-cloud infrastructures. However, the research also acknowledges potential limitations and ethical considerations, paving the way for future advancements in this critical area of cybersecurity.

Cloud Guardian: A Comprehensive Approach to Cloud Security Posture Management with Automated Multi-Cloud and Container Security

As organizations increasingly adopt multi-cloud environments and containerized applications to improve scalability and operational efficiency, managing security across these diverse platforms has become a critical challenge. Traditional security tools and manual processes are insufficient to handle the complexity and dynamic nature of modern cloud infrastructures. This research introduces Cloud Guardian, a cloud security posture management (CSPM) platform that automates security assessments, policy enforcement, and threat detection across multi-cloud platforms and containerized environments. Cloud Guardian addresses the key security challenges associated with multi-cloud and container environments, including misconfigurations, unpatched vulnerabilities, and inconsistent security policies. By integrating real-time threat intelligence and automated remediation capabilities, Cloud Guardian provides continuous protection, ensuring compliance with industry standards such as CIS and NIST. Additionally, the platform’s self-healing mechanisms and rollback capabilities allow for secure and uninterrupted operations by automatically fixing security issues and reverting changes if necessary. This study evaluates Cloud Guardian's effectiveness in securing complex cloud infrastructures through real-world case studies. The findings demonstrate significant improvements in operational efficiency, compliance, and security posture, reducing the risk of breaches and operational disruptions. Cloud Guardian offers a scalable and automated solution for organizations seeking to safeguard their cloud and container environments while minimizing manual intervention and ensuring continuous security and compliance.

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

The Current Status and Promotional Strategies for Cloud Migration of Hospital Information Systems in China: Strengths, Weaknesses, Opportunities, and Threats Analysis.

In the 21st century, Chinese hospitals have witnessed innovative medical business models, such as online diagnosis and treatment, cross-regional multidepartment consultation, and real-time sharing of medical test results, that surpass traditional hospital information systems (HISs). The introduction of cloud computing provides an excellent opportunity for hospitals to address these challenges. However, there is currently no comprehensive research assessing the cloud migration of HISs in China. This lack may hinder the widespread adoption and secure implementation of cloud computing in hospitals. The objective of this study is to comprehensively assess external and internal factors influencing the cloud migration of HISs in China and propose promotional strategies. Academic articles from January 1, 2007, to February 21, 2023, on the topic were searched in PubMed and HuiyiMd databases, and relevant documents such as national policy documents, white papers, and survey reports were collected from authoritative sources for analysis. A systematic assessment of factors influencing cloud migration of HISs in China was conducted by combining a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and literature review methods. Then, various promotional strategies based on different combinations of external and internal factors were proposed. After conducting a thorough search and review, this study included 94 academic articles and 37 relevant documents. The analysis of these documents reveals the increasing application of and research on cloud computing in Chinese hospitals, and that it has expanded to 22 disciplinary domains. However, more than half (n=49, 52%) of the documents primarily focused on task-specific cloud-based systems in hospitals, while only 22% (n=21 articles) discussed integrated cloud platforms shared across the entire hospital, medical alliance, or region. The SWOT analysis showed that cloud computing adoption in Chinese hospitals benefits from policy support, capital investment, and social demand for new technology. However, it also faces threats like loss of digital sovereignty, supplier competition, cyber risks, and insufficient supervision. Factors driving cloud migration for HISs include medical big data analytics and use, interdisciplinary collaboration, health-centered medical service provision, and successful cases. Barriers include system complexity, security threats, lack of strategic planning and resource allocation, relevant personnel shortages, and inadequate investment. This study proposes 4 promotional strategies: encouraging more hospitals to migrate, enhancing hospitals' capabilities for migration, establishing a provincial-level unified medical hybrid multi-cloud platform, strengthening legal frameworks, and providing robust technical support. Cloud computing is an innovative technology that has gained significant attention from both the Chinese government and the global community. In order to effectively support the rapid growth of a novel, health-centered medical industry, it is imperative for Chinese health authorities and hospitals to seize this opportunity by implementing comprehensive strategies aimed at encouraging hospitals to migrate their HISs to the cloud.

Evolution of Database Management System in Hybrid and Multi Cloud Platform: Challenges, Solutions and Innovations

The rapid advancement in cloud computing has catalyzed the evolution of database management systems (DBMS), particularly in the context of hybrid and multi-cloud platforms. This review paper examines the transformative journey of DBMS, focusing on the challenges, solutions, and innovations that have emerged in hybrid and multi-cloud environments. Key challenges include data consistency, security, latency, and cost management, which are compounded by the heterogeneity and distributed nature of these platforms. The paper discusses state-of-the-art solutions, such as advanced data replication techniques, automated policy-driven management, and robust security protocols, that address these challenges. Furthermore, it highlights groundbreaking innovations in database architecture, including cloud-native DBMS, serverless databases, and AI-driven database optimization, which are reshaping the landscape. By providing a comprehensive analysis of these developments, this review aims to offer valuable insights into the current trends and future directions of DBMS in hybrid and multi-cloud platforms, serving as a resource for researchers and practitioners in the field.

Dragonfly soft-computing approach for workload scheduling resource utilization maximization using multi-cloud platform

The execution of the real-time workload in the multi-cloud platform with the Service Level Agreement (SLA) requirements is a challenging task. Existing workload scheduling models have addressed issues related to minimizing execution time, cost, and energy with application reliability prerequisite. However, these models are not efficient in maximizing resource utilization under a multi-cloud platform. In addressing resource utilization issues, this paper presents a Workload Scheduling Resource Utilization Maximization (WS-RUM) technique for the multi-cloud platform. The WS-RUM technique leverages a multi-objective such as energy, processing efficiency, and fault-tolerant offloading mechanism employing a dragonfly soft computing algorithm. The WS-RUM improves resource utilization by minimizing both energy and processing time for real-time workload execution in comparison with existing workload execution.

Handling security issues by using homomorphic encryption in multi-cloud environment

Taking advantage of the high performance and powerful data processing capabilities of cloud computing technology, externalizing data to the cloud platform is considered as an inevitable trend in the digital field today. However, ensuring the security and privacy of data remains a major challenge. To overcome this drawback, a multi-cloud platform is proposed to improve privacy and high availability of data. A multi-cloud platform that integrates public, private, and managed clouds with a single user interface. Cloud-hosted data is distributed among different data centers in a multi-cloud environment based on cloud reliability and data sensitivity. In terms of security, current encryption algorithms are considered to be very efficient, but it requires a lot of resources to handle this, which is expensive and time consuming. In addition, they also make the data impossible to process without first decoding. To be specific, traditional public key encryption requires data to be decrypted before it can be analyzed or manipulated. In contrast, homomorphic encryption is an encryption method that allows data to be encrypted while it is being processed and manipulated. It allows user or a third party, which can be cloud provider, to apply functions on encrypted data without revealing the data's values. In this paper, we explore existing multi-cloud-based security solutions using homomorphic encryption to identify open issues and opportunities for further enhancement.

Towards a Reference Architecture for Semantic Interoperability in Multi-Cloud Platforms

Towards a Reference Architecture for Semantic Interoperability in Multi-Cloud Platforms

NFDI4Biodiversity: a German infrastructure for biodiversity data

Digital data have become an indispensable basis for biodiversity research. Sustainable curation, archiving, accessibility and integrability according to the FAIR principles ("Findable, Accessible, Interoperable and Reusable", Wilkinson et al. 2016) are essential for re-use to answer pressing questions in a rapidly changing environment. As part of the German multidisciplinary National Research Data Infrastructure (NFDI), the NFDI4Biodiversity consortium with 49 partners, spanning a broad spectrum from academia, to agencies, learned societies, and citizen science, has set itself the goal of providing a sustainable data infrastructure for biodiversity research. NFDI4Biodiversity builds on the German Federation for Biological Data (GFBio) project (2014–2021) and the GFBio e.V. founded in it, both organisationally and in the provision of services. These include a data submission and archiving system, support for the creation of data management plans and certification, portal functions with extensive data visualization and terminology services flanked by helpdesk, support and outreach activities*1 (Diepenbroek et al. 2014). Within the framework of NFDI4Biodiversity, these services will be expanded, based on (and calibrated by) the requirements of 23 concrete use cases from manifold biodiversity research domains. A central new component is the development of a capable multi-cloud platform, the "Research Data Commons" (RDC) where data can be aggregated, semantically linked and enriched with external services (Glöckner et al. 2020). Alongside the development of the services, the potential for joint use of standards and service components will be exploited through cooperation with existing data infrastructures. In addition to other NFDI consortia, international infrastructures and comparable national initiatives will play a special role; this process was already started with a symposium during the 2019 Biodiversity Next conference.

Multi-Cloud Integration Security Framework Using Honeypots

This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.

SLA-WS: SLA-based workload scheduling technique in multi-cloud platform

SLA-WS: SLA-based workload scheduling technique in multi-cloud platform

An archetype for mitigating the security threats in multi-cloud environment by implementing tree-based next-generation firewalls

Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases.

An efficient and secure protocol for checking remote data integrity in multi-cloud environment

In cloud environment, huge quantity of data has been generated at each and every second. In order to manage the data, cloud service provider makes use of multi-cloud platform to fulfill the requirement. The service provider cooperatively operates altogether for the accessibility of resources and are improvised by implementing the dynamic operation that can run at a time through the Multi-cloud environment. This paper presents a Dynamic Level Based Integrity Checking Protocol (DA-ICP) for storing data in multicloud environment. The proposed method introduces Provable Data Possession (PDP) approach which enables a user who outsources the data at an untrusted multi-cloud for ensuring that the server possesses the original data without downloading it. This model creates a probabilistic proof of possession by sampling an arbitrary collection of blocks from server that considerably minimizes the cost. The effective and secured outsourced data has been resolved using public key cryptography and undergo encryption using Efficient-PDP (EPDP). During experimentation, the presented DA-ICP shows a maximum accuracy of 96.78%. The proposed method uses Multi-cloud in DA-ICP which produces an efficient output than other existing techniques.

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

ObjectivesIn this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.Materials and MethodsWe utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.ResultsWe highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.Discussion and conclusionWe are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.

A Multicloud-Model-Based Many-Objective Intelligent Algorithm for Efficient Task Scheduling in Internet of Things

Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data are used maliciously, the security loss may be further caused. Multicloud is a high-performance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multicloud and task scheduling in IoT, this article constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Furthermore, this article presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

Distributed bandwidth selection approach for cooperative peer to peer multi-cloud platform

Network function virtualization (NFV) is a well-accepted software-defined network (SDN) architecture concept. NFV is severally used in cloud platform to enhance the utilization of available network resources. The concept behind NFV is a design of software process architecture with a bunch of collaborative resource utilization functions. The progression of platform providers builds a communication service plan with the help of virtual machines. We propose biogeography-based optimization (BBO) technique in order to enrich virtual network functions (VNFs) to utilize available bandwidth resource in an energy-efficient and cost-effective manner. The cooperative bandwidth sharing approach using BBO reduces delay mean at the time of supervision the impending requests in a multi-cloud environment. Experimental results validate that the utilization of available bandwidth has increased using the proposed scheme with respect to the single user-dedicated resource allocation and multi-user dedicated resource allocation. Delay means, as well as subscription cost, has reduced compared to the existing methods. The correctness of the investigational outcome has been established using an experimental setup along with the proposed algorithm.

Use Triple H-AVATAR Technology for Education in Online Multi-cloud Platform Lab

One could argue that the traditional scheme of education needs to change. New forms of education should be based on mobility, interactivity and cooperation of the participants of the education process. To this end, there should be new approaches education. Now they say about study-oriented re-searching without temporal and spatial boundaries, which requires the use of new education technologies—H Avatar. Currently there is no software system that would realize the full intellectual tasks described above. The reason for this lies in the simplified model of learning, which is associated with the unification of the parameters of the model education and the study material. Note that even this approach requires considerable effort in the implementation of both the local and remote versions.

A Review of Hosting Enterprise SaaS with IaC on Multi-cloud Platforms

This paper explores the concepts of Enterprise Software as a Service (SaaS), the reasons for hosting a SaaS solution on a multi-cloud platform, and the benefit of using Infrastructure as Code (IaC) on a multi-cloud for quick provisioning. The paper also details the various challenges faced while adopting a multi-cloud solution. In conclusion, the paper recommends using multi-cloud and IaC to host all Enterprise SaaS solutions.

A Scalable Multicloud Storage Architecture for Cloud-Supported Medical Internet of Things

Nowadays, cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart medical systems, where the limitations of Internet of Things (IoT)-associated medical devices in terms of data access, storage, scalability, and computing are solved through the use of cloud computing architectures. However, with the rapid development of medical equipment and the increasing number of medical devices, it will be extremely difficult to program or manage such an expanding and massive medical IoT system in traditional single-cloud platforms. In this article, we design and implement a multicloud framework for building OpenStack-based platform for medical IoT, referred to as the tri-storage failure recovery system (Tri-SFRS). To implement Tri-SFRS, we combine several techniques to achieve this reduction in effort, including a multicloud cascading architecture, a low-overhead native testing framework, a medical data storage-backup mechanism, and snapshot-volume cascaded operations for b-ultrasonic data. Tri-SFRS is also able to simultaneously enable resource management specialization. Tri-SFRS has been designed as a native component in the OpenStack platform, and it demonstrates the degree of native OpenStack multicloud platform management by our proposed cascading framework. Comparing with the traditional single-cloud OpenStack platform, Tri-SFRS can reduce the resource-request processing latency from B ultrasonic machines by up to 20%. Our experiments also demonstrate the broad applicability of Tri-SFRS.

Development and Study of Algorithms for the Formation of Rules for Network Security Nodes in the Multi-Cloud Platform

As part of the study, existing solutions aimed at ensuring the security of the network perimeter of the multi-cloud platform were considered. It is established that the most acute problem is the effective formation of rules on firewalls. Existing approaches do not allow optimizing the list of rules on nodes that control access to the network. The aim of the study is to increase the effectiveness of firewall tools by conflict-free optimization of security rules and the use of a neural network approach in software-defined networks. The proposed solution is based on the sharing of intelligent mathematical approaches and modern technologies of virtualization of network functions. In the course of experimental studies, a comparative analysis of the traditional means of rule formation, the neural network approach, and the genetic algorithm was carried out. It is recommended to use the multilayer perceptron neural network classifier for automatic construction of network security rules since it gives the best results in terms of performance. It is also recommended to reduce the size of the firewall security rule list using the Kohonen network, as this tool shows the best performance. A conflict-free optimization algorithm was introduced into the designed architecture, which produces finite optimization by ranking and deriving the most common exceptions from large restrictive rules, which allows increasing protection against attacks that are aimed at identifying security rules at the bottom of the firewall list. On the basis of the proposed solution, the adaptive firewall module was implemented as part of the research.