Cloud Environment Research Articles

Cloud Elasticity of Microservices‐Based Applications: A Survey

ABSTRACTElasticity is an essential treatment in Cloud environment employed in academic and industrial contexts. The main purpose of elasticity is to reduce the deployment cost while optimizing computing resources. Multiple studies were conducted to tackle classic applications using monolithic architecture deployed with virtual machines (VMs). However, with the spread of microservice pattern, recent studies have been investigating this new trend using containers. This paper classifies and discusses existing approaches dealing with cloud elasticity. It provides a novel taxonomy for elastic approaches while focusing on microservices‐based solutions. We additionally specify the strength and the shortcomings of each class of works. As a conclusion, we report the challenges for microservices‐based applications elasticity and provide requirements for future investigations.

Securing big healthcare data using attribute and honey-based encryption in cloud environment

Securing big healthcare data using attribute and honey-based encryption in cloud environment

Multi-objective energy aware task scheduling using Orthogonal Learning Particle Swarm Optimization on cloud environment

Multi-objective energy aware task scheduling using Orthogonal Learning Particle Swarm Optimization on cloud environment

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

High Availability Database Infrastructure with Galera and HAProxy

High Availability (HA) is crucial in the cloud environment to render the services according to the Service Level Agreement (SLA) to the customers. This paper provides an novel solution for 99.99% high availability and scalability of database infrastructures using Galera Cluster, along with HAProxy. The Galera Cluster delivers synchronous multi-master replication over multiple nodes, guaranteed data consistency while in real-time and for each transaction, high availability through automatic failover. HAProxy strikes a balance here by distributing the traffic across nodes and achieving optimal load distribution with less or minor effect on downtime. Real-time monitoring and alerts are also enabled by using Prometheus & Grafana to monitor cluster health as well performance.

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Layered quantum secret sharing scheme for private data in cloud environment system

Layered quantum secret sharing scheme for private data in cloud environment system

Data management solutions for real-time analytics in retail cloud environments

In today's rapidly evolving retail landscape, the importance of real-time data analytics has surged, driven by the need for businesses to make informed decisions and enhance customer experiences. This paper explores the critical role of effective data management solutions within cloud environments, focusing on retailers' unique challenges, such as data integration, latency, security, and storage costs. It presents practical solutions, including data partitioning, caching, AI-driven analytics, and the strategic use of data warehousing and data lakes. By adopting hybrid data management models, retailers can optimize their data processes and leverage real-time insights to drive operational efficiency and competitive advantage. This paper offers valuable recommendations for retail businesses and data architects to enhance their cloud-based data management strategies, ensuring they can navigate the complexities of real-time data effectively and sustainably. Keywords: Real-time analytics, Data management, Retail, Cloud environments, Data integration, AI-driven solutions.

Comprehensive review of additive manufacturing lifecycle optimization in a cloud environment: from distributed manufacturing to postprocessing

Purpose This paper aims to offer a comprehensive review and categorization of production optimization throughout the additive manufacturing lifecycle in a cloud environment. It aims to provide a structured approach to identifying and addressing issues. Design/methodology/approach This paper systematically reviews 75 technical papers on cloud manufacturing, nesting, scheduling and postprocessing in additive manufacturing. This includes a detailed discussion of the key issues. Findings This paper introduces a production framework for the entire lifecycle of additive manufacturing in a cloud environment. This framework aids in problem identification and decision-making based on the process flow. It provides an integrated view from cloud to postprocessing, examining decision interdependencies and enhancing problem identification and organization. Originality/value To the best of the authors’ knowledge, this paper is the first to review the complete lifecycle of additive manufacturing, emphasizing the often-overlooked aspects of postprocessing and cloud manufacturing. It offers a comprehensive study of lifecycle optimization challenges and suggests ways to streamline the production process.

MinCloud: Trusted and transferable MinHash-based framework for unknown malware detection for Linux cloud environments

Linux clouds have become an attractive target for cyber-attacks. However, existing detection solutions for Linux clouds have variety of limitations. Some of the solutions are untrusted, incapable of detecting unknown malware, or rely on a human expert to define the features. Other solutions are trusted but require a large amount of computational resources or have a limited ability to detect rootkits, fileless malware, or malware on a different server. In this study, we propose MinCloud, a trusted and transferable MinHash-based framework for unknown malware detection in Linux virtual servers that overcomes the limitations of existing solutions. In the first stage, we acquired volatile memory dumps from virtual servers by querying the hypervisor in a trusted manner and then analyzed them using the MinHash method. Finally, the MinHash characteristics are harnessed by applying machine learning classifiers to achieve precise malware detection. MinCloud was evaluated on widely used Linux virtual servers, various benign and malicious applications, and 23,000 volatile memory dumps, each representing different behaviors of the examined servers and the executed applications over time. MinCloud's evaluation shows it can (1) detect unknown malware, (2) classify unknown malware according to its malware category, (3) detect fileless attacks and rootkit malware, and (4) provide accurately transfer detection between different Linux servers. MinCloud outperformed state-of-the-art trusted detection methods and commonly used antiviruses.

Zero Trust Architecture in Cloud Computing: A Paradigm Shift in Platform Engineering Security

This article explores the paradigm shift in platform engineering security brought about by Zero Trust Architecture (ZTA) in cloud computing environments. As organizations increasingly adopt cloud-native architectures and microservices, traditional perimeter-based security models prove inadequate for protecting complex, distributed systems. The article examines the core principles of ZTA, including robust identity and access management, least privilege access, micro-segmentation, continuous monitoring, and secure communication. It delves into the implementation strategies of ZTA in platform engineering, highlighting its benefits such as enhanced security, improved compliance, increased resilience against threats, adaptability to dynamic environments, and improved visibility. The article also addresses the challenges of implementing ZTA, including complexity, potential performance concerns, user experience considerations, cost implications, and the risk of vendor lock-in. Through an analysis of industry trends, adoption rates, and quantified benefits, the article demonstrates how ZTA addresses critical security challenges in modern cloud environments, ultimately enabling organizations to create more resilient, compliant, and adaptable platforms in the face of evolving cyber threats.

Blockchain With Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography for Securing Image

ABSTRACTNowadays, the image security is one of the most challenging issues to address the technological age. Security is the primary issue in data management and transmission because of the original data form that is read, abused and destroyed. The cloud companies struggle to secure the file. The cloud security is the major concern in cloud computing context. Numerous researches have been presented so far to protect the cloud environment. But, none of them provides the sufficient security. Therefore, this paper proposes a Blockchain‐based technique for Image Security that combines Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography (BC‐SIE‐HAPCNN‐FODCE). The Flickr30k dataset is used to collect the input images. At that point, cryptographic pixel values of picture are kept on blockchain to defend security of picture information. It uses Delegated Proof of Stake Consensus (DT‐DPoS) approach appointed confirmation of stake agreement approach. The performance parameters, like processing time, reaction time, runtime, correlation coefficient analysis, entropy analysis, mean square error, and availability are used to determine the efficacy of the proposed BC‐SIE‐HAPCNN‐FODCE approach. The performance of the proposed technique attains 18.81%, 32.05%, and 22.28% higher correlation coefficient and 25.38%, 20.81%, and 26.04% higher entropy compared with existing methods, such as Multiple Rossler lightweight Logistic sine mapping dependent Federated convolutional method with cyber blockchain in medical image encryption (BC‐SIE‐FCAL‐MRLLSM), color image encryption under Hénon‐zigzag map with chaotic restricted Boltzmann machine over Blockchain (BC‐SIE‐CRBM‐HZM) and blockchain‐assisted safe picture transmission along detection method on Internet of Medical Things Environment (BC‐SIE‐ECC‐DBN), respectively.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Big Data in Cloud Computing

The convergence of Big Data and cloud computing has revolutionized the way organizations process, store, and analyze large datasets. This paper explores the synergistic relationship between these two transformative technologies, highlighting their impact on business operations, decision-making, and innovation. By leveraging cloud platforms, enterprises can harness the scalability, flexibility, and cost-efficiency of distributed computing to manage vast volumes of data generated from diverse sources. Cloud-based big data analytics enables real-time insights, which drive strategic actions, improve customer experiences, and foster competitive advantages. The research delves into key cloud architectures, data management techniques, and analytics tools that underpin big data solutions, while addressing challenges such as data security, privacy, and compliance. Furthermore, the study reviews industry use cases across various sectors, illustrating how big data in cloud environments enhances productivity and innovation. The findings underscore the pivotal role of cloud computing in unlocking the full potential of big data, offering a roadmap for businesses aiming to capitalize on data-driven strategies.

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.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.

Enhanced Ant Colony Based VM Selection and Consolidation for Energy Conservation

Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments.

Multi-agent Deep Reinforcement Learning for cloud-based digital twins in power grid management

As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable accurate simulations and proactive management, but the vast amount of data generated by DT systems poses significant challenges for processing and analysis. Cloud computing offers a flexible solution by offloading these computational tasks to distributed resources, allowing for real-time analysis and scalable operations. However, this approach introduces complexities in task distribution and maintaining quality of service. Recent efforts have applied Deep Reinforcement Learning (DRL) to address these challenges, primarily using single-agent methods. However, these methods struggle with scalability and performance in increasingly complex cloud environments. To overcome these limitations, we propose an efficient task scheduling framework based on Multi-Agent Deep Q-Network (MADQN) principles, specifically designed to optimize both response times and operational costs. We provide a comprehensive design overview of our approach and conduct a thorough evaluation of its performance. The experimental results clearly indicate that our approach can considerably reduce response times amd lower operational costs compared to current methods, including state-of-the-art single-agent approaches.

A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System

Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal information. Input and output data of a learning model may include sensitive data that must be protected. Adversaries could intercept and exploit this data to infer more sensitive data or even to determine the structure of the prediction model. To guarantee data privacy, one option could be encrypting data and making inferences over encrypted data. This strategy would be challenging for learning models that now must receive encrypted data, make inferences over encrypted data, and deliver encrypted data. To address this issue, this paper presents a privacy-preserving machine learning approach using Fully Homomorphic Encryption (FHE) for a model that predicts risk levels of suffering a traffic accident. Despite the limitations of experimenting with FHE on machine learning models using a low-performance computer, limitations that are undoubtedly overcome by using high-performance computational infrastructure, we built some encrypted models. Among the encrypted models based on Decision Trees, Random Forests, XGBoost, and Fully Connected Neural Networks (FCNN), the model based on FCNN reached the highest accuracy (80.1%) for the lowest inference time (8.476 s).