Hybrid Cloud Environment Research Articles

Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation

Virtualization is the process of sharing physical machine resources into multiple Virtual Machines (VMs) for the effective utilization of resources. The major issues are in handling physical resources such as high demand of resources, infrastructure issues and operational issues. Also violating Service Level Agreement (SLA) is another problem to lead the violations. In this case, VM consolidation method is used to migrate the VMs and optimize the efficient way to use the resources. So we get minimized energy consumptions. Existing methods has increased energy consumption and overhead issues. In this paper we propose adaptive backtracking methods to provide VM consolidation based on less energy consumptions. We use Adaptive Hill Climbing and Pursuit algorithms to consolidate the VMs and use the VMs with efficient energy consumption. We implement this simulations using Matlab in Hybrid Cloud Environment. The performance factors are obtained with less energy consumption and compare the results with existing methods. Overall our proposed system is achieving 95% of accuracy index with lower energy consumption such as 28%, 30% and 32% with multiple VMs consolidations.

HYBRID CLOUD DATABASES FOR BIG DATA ANALYTICS: A REVIEW OF ARCHITECTURE, PERFORMANCE, AND COST EFFICIENCY

The increasing reliance on big data analytics has driven organizations to seek more flexible, scalable, and cost-effective data management solutions, with hybrid cloud databases emerging as a prominent option. Hybrid cloud databases integrate both public and private cloud environments, allowing businesses to balance the scalability and cost advantages of public clouds with the security and control provided by private clouds. This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, examines the architecture, performance, cost efficiency, security, and technological advancements of hybrid cloud databases. The study synthesizes findings from 40 peer-reviewed articles published between 2015 and 2024, focusing on key factors that affect the performance of hybrid cloud databases, such as workload distribution, network latency, and the use of advanced data analytics tools like Hadoop and Spark. The review also explores cost-saving mechanisms, including dynamic resource scaling and pay-as-you-go pricing models, which help organizations reduce infrastructure costs by up to 28%. Additionally, it discusses the security risks and privacy concerns associated with hybrid cloud environments, highlighting the effectiveness of encryption protocols and identity access management systems in mitigating data breaches by as much as 40%. Furthermore, the review identifies emerging trends in the integration of machine learning and artificial intelligence (AI), which have significantly enhanced system automation and resource optimization in hybrid cloud infrastructures, leading to performance improvements of up to 45%.

RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key generation, decryption, and ciphertext update while assuring their security. By implementing RT-PPS, organizations can take advantage of the benefits of IoT data while protecting the privacy of their users and maintaining the security of their systems.

Adaptive Security in Hybrid Cloud Environments: Leveraging AI and Machine Learning

Adaptive Security in Hybrid Cloud Environments: Leveraging AI and Machine Learning

Authorized Redundant Check Support in a Hybrid Cloud Environment

A hybrid cloud is made up of public and private clouds connected by standardized or proprietary technologies to provide data and application mobility. The deduplication with uneven privileges issue in cloud computing is addressed efficiently. an architecture for a hybrid cloud that employs both public and private clouds, with the data owners only using the public cloud for storage and the private cloud for data administration. To make data management in cloud computing scalable, duplication has lately become a commonly employed method. Deduplication reduces the amount of bandwidth you need, speeds up data transfers, and keeps your need for cloud storage to a minimum. To enable authorized duplicate checks in hybrid cloud architecture, the proposed system comprises a variety of new deduplication structures. Data confidentiality was protected before outsourcing by employing the convergent encryption method. A system authorized to use deduplication supports differential authorization duplicate checks. In the authorized duplication check approach, testbed tests are carried out using a prototype as proof of concept.

English

English

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.

Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier

Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. Intelligent Genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized Intelligent Genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized Intelligent Genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized Intelligent Genetic Load Balancer system performance with existing simulations results and compared the results.

Authorized Redundant Check Support in a Hybrid Cloud Environment

Data deduplication is one of the most significant data contraction ways for removing indistinguishable clones of recreating data, and it's generally used in pall storehouse to minimise storehouse space and save bandwidth. The coincident encryption approach has been developed to cipher the data before outsourcing to insure the confidentiality of sensitive data while easing deduplication. This work is the first attempt to explicitly address the content of authorised data deduplication in order to ameliorate data security. In discrepancy to standard deduplication systems, the discriminational boons of druggies are taken into account in indistinguishable check in addition to the data itself. In addition, we describe numerous innovative deduplication infrastructures that give authorised indistinguishable check in a cold-blooded pallarchitecture. Our approach is secure in terms of the delineations stated in the proposed security model, according to security analysis. We apply a prototype of our proposed authorised indistinguishable check medium as a evidence of conception and take over testbed tests with it. We demonstrate that our proposed authorised indistinguishable check fashion has a low outflow when compared to typical operations

Efficient algorithm for replanning web service composition

Web-service-composition (WSC) workload execution inside a hybrid cloud environment is challenging. A dynamic approach for allocating resources to various tasks, as well as associated sub-tasks having a satisfactory quality-ofservice (QoS) requirement, is necessary for the present real-time demand. As a result of focusing primarily on decreasing processing time as well as cost, current approaches improve latency as well as energy while executing a given workload. This study introduces an efficient re-planning (ERP) algorithm for running many scientific workloads inside a heterogeneous cloud environment, which is designed to address some of the shortcomings of previous approaches. With a changing workload, this study details a technique to improve the WSC's availability as well as robustness. The workload's processing energy requirements are reduced as a result. The montageworkflow has been used to validate the research findings. Comparison with the current heterogeneous earliest finish time (QL-HEFT) algorithm demonstrates that its ERP-WSC approach is much more efficient and reliable.

Performance Testing for Hybrid Cloud Environments

Performance Testing for Hybrid Cloud Environments

Enhancing Virtual Desktop Infrastructure with Hybrid Cloud Environments and GPU Acceleration

This research paper focuses on the implementation and testing of a cloud based Virtual Desktop Infrastructure (VDI) that combines both cloud technology and GPU acceleration. The paper explores how various technologies, such, as Citrix Virtual Apps, Windows 11 Virtual Desktops, VMware vSphere and NetApp Storage can be integrated within a data center framework. The study emphasizes the importance of hybrid cloud environments in addressing scalability, security and disaster recovery challenges faced by VDI systems. In particular it highlights the significance of NVIDIA A40 cards for GPU accelerated Virtual Desktop Infrastructure (VDI) and the use of SPECviewperf 2020 to evaluate performance. The validation process involves conducting tests, on disaster recovery scenarios using NetApp SnapMirror while also evaluating the performance of NetApp AFF A400 storage systems. The results demonstrate that this system effectively handles latency issues and performs well in large scale VDI deployments.

LCDT-M: Log-Cluster DDoS Tree Mitigation Framework Using SDN in the Cloud Environment

In the cloud computing platform, DDoS (Distributed Denial-of-service) attacks are one of the most commonly occurring attacks. Research studies on DDoS mitigation rarely considered the data shift problem in real-time implementation. Concurrently, existing studies have attempted to perform DDoS attack detection. Nevertheless, they have been deficient regarding the detection rate. Hence, the proposed study proposes a novel DDoS mitigation scheme using LCDT-M (Log-Cluster DDoS Tree Mitigation) framework for the hybrid cloud environment. LCDT-M detects and mitigates DDoS attacks in the Software-Defined Network (SDN) based cloud environment. The LCDT-M comprises three algorithms: GFS (Greedy Feature Selection), TLMC (Two Log Mean Clustering), and DM (Detection-Mitigation) based on DT (Decision Tree) to optimize the detection of DDoS attacks along with mitigation in SDN. The study simulated the defined cloud environment and considered the data shift problem during the real-time implementation. As a result, the proposed architecture achieved an accuracy of about 99.83%, confirming its superior performance.

Information security for big data using the NTRUEncrypt method

Cloud computing processes vast quantities of data and offers a variety of flexible, secure, on-demand, and cost-effective collaboration options for consumers. Due to the increasing prevalence of hosted services, data security has become an increasingly critical concern. Hadoop, the engine at the heart of cloud computing, causes serious problems for the cloud. Any public, private, or hybrid cloud environment can use this security solution without any hassle (IaaS). Furthermore, it is compatible with the vast majority of Cloud computing's capabilities. Increase cloud security using NTRU encryption. This study made advantage of the (NTRUEncrypt) algorithms residing in Hadoop to speed up the file encryption and decryption processes. If HDFS is engaged in the Map Task, then HDFS will take care of both the encryption and decryption processes. Data on the cloud can be kept private and secure thanks to the proposed protection technique, which makes use of cryptography. Combining the proposed technique with preexisting infrastructure and web-based.

Data security in cloud environment using cryptographic mechanism

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving. The algorithm uses opposition-based learning to generate initial populations for faster convergence. Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range. By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.

An Implementation of Divide and Conquer Clustering Technique for Improving the Interoperability in Hybrid Cloud Environment

Cloud computing provides users with pool of resources ubiquitously on demand. While the resources are provided to the users, interoperability needs to be considered. Interoperability is the ability of the cloud environment to transfer the data internally or between the data centers seamlessly. Interoperability is the least studied issue in the field of cloud computing. This paper implements hybrid cloud as a solution to interoperability. Hybrid cloud is chosen for its powerful combination of high configured and secured private clouds and fast accessible and scalable public clouds. The interoperability is then proposed to be enhanced by implementing divide and conquer algorithm of clustering in hybrid cloud.

Creating Transformational Media Solutions Using 5G Edge Computing

This article discusses how to create effective 5G edge media solutions. We provide a high-level overview of edge computing, key challenges for implementing an end-to-end media edge solution, and the benefits of such solutions. Media use cases impacted by 5G will be discussed. We also examine the different layers of the edge architecture including the application and network layer integrated with far-edge devices, multiaccess edge compute (MEC), and hybrid cloud environments built on a 5G network. 5G media applications integrated with advanced network functions such as slicing with closed-loop automation to optimize network and application functions for media solutions will be discussed. We will present the above in the context of implementation and conclude with lessons learned and key challenges that need to be addressed in the future.

SDMTA: Attack Detection and Mitigation Mechanism for DDoS Vulnerabilities in Hybrid Cloud Environment

In a distributed cloud context, distributed denial of service (DDoS) attacks are widespread. The services are rendered unavailable to legitimate users as a result of the overwhelming traffic, resulting in financial losses. There are possible obstacles, although several researchers have established various mitigation measures. Initially, Software-defined networking technology was revealed to protect businesses from DDoS attacks. DDoS attacks cause server outages and financial losses due to service unavailability. Meeting of service-level agreement with the customers remains a challenge. In this article, the scattered denial-of-service mitigation tree architecture (SDMTA) is used to propose a novel DDoS mitigation strategy for the hybrid cloud environment. To enable detection procedures, the proposed SDMTA mitigation architecture includes integrated network monitoring. The suggested and existing state-of-the-art models’ detection rates over the input dataset were estimated. When compared to the existing state-of-the-art model, the system's accuracy, specificity, and sensitivity were found to be 99.7%, 98.32%, and 99.92%, respectively.

Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage

The escalating growth of distributed big data in hybrid cloud storage architecture introduces a new set of challenges. Constantly, content enrichment puts pressure on capacity. Nonetheless, the explosion of user data places a significant strain on broadband and storage capacity. Consequently, many cloud storage providers will implement deduplication to compress data, reduce transfer bandwidth, and reduce cloud storage space. In cloud storage systems, it is a data compression and storage optimization method. By locating and removing redundant data, it can save storage space and bandwidth. An MTHDedup deduplication strategy based on the Merkle hash tree is presented in a hybrid cloud environment to address the issue of convergent encryption algorithms being susceptible to brute-force attacks and ciphertext computation time overhead. Merkle hash trees are constructed using additional encryption algorithms to generate encryption keys during file- and block-level deduplication, ensuring that generated ciphertexts are unpredictable. The method is effective against both internal and external brute-force attacks, thereby increasing data security. Our method reduces the computational burden of ciphertext generation and the key storage space, and the performance advantage increases with the number of privilege sets.