Cloud Infrastructure Research Articles

DDOS Protection System for Cloud: Architecture and Tool

Distributed Denial of Service (DDoS) attacks, in the realm of cloud computing, have become the most serious threats to the availability and reliability of services. However, these attacks become the direct cause of the target system slowdown or shutdown by saturating the servers with a massive amount of traffic. This harms the cloud-based applications' performance. As cloud infrastructure has become the backbone of every company, the development of effective and scalable DDoS protection mechanisms to ensure the continuity of services is a must. This paper gives a clear overview of the architecture and equipment involved in the cloud-based DDoS protection system. We look at the different layers of protection such as traffic filtering, rate-limiting, anomaly detection, and the application of security services native to the cloud. Examples of such Web Application Firewalls (WAFs), Content Delivery Networks (CDNs), and Cloud Security Posture Management (CSPM) systems. The architecture employs distributed and multi-layered security solutions for detecting and mitigating the attack in real-time whilst keeping the legitimate users safe from any effects

Optimizing machine learning pipelines for cost and performance using cloud

Abstract–This article explores comprehensive strategies for optimizing machine learning pipelines in cloud environments, focusing on IP protection systems. It addresses the critical challenges of balancing performance, cost, and scalability while maintaining robust security measures. The discussion encompasses various optimization techniques, including cloud infrastructure management, batch processing implementations, asynchronous model invocation, and memory management strategies. Through examination of real-world implementations and research findings, the article demonstrates how organizations can leverage cloud-native services, advanced compression techniques, and intelligent resource allocation to enhance their ML operations. The article provides practical insights into achieving cost-effective scaling while maintaining high-performance standards, offering valuable guidance for engineers and architects working with cloud-based machine learning systems.

Green Computing Optimization for Multi-Region Streaming Platforms

Global streaming services are always under pressure to control their resource usage effectively while still offering millions of users worldwide flawless experiences. As user demands for continuous, high-quality content increase, it is becoming increasingly important to strike the right balance between low latency, high availability, and resource efficiency. However, the infrastructure required to meet these demands often results in significant energy consumption and operational costs, presenting a major challenge for sustainability. The application of green computing principles to multi-region cloud infrastructure optimization is examined in this article, with an emphasis on tactics that minimize energy consumption and operational costs without compromising the performance that users rely on. Platforms can preserve their competitive advantage while making significant progress toward environmental responsibility by implementing more intelligent, energy-efficient procedures.

Optimizing healthcare big data performance through regional computing

The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast quantities of healthcare data. Efficient and timely analysis of this data is critical for enhancing patient outcomes and optimizing care delivery. Real-time processing of Healthcare Big Data (HBD) offers significant potential for improved diagnostics, continuous monitoring, and effective surgical interventions. However, conventional cloud-based processing systems face challenges due to the sheer volume and time-sensitive nature of this data. The migration of large datasets to centralized cloud infrastructures often results in latency, which impedes real-time applications. Furthermore, network congestion exacerbates these challenges, delaying access to vital insights necessary for informed decision-making. Such limitations hinder healthcare professionals from fully leveraging the capabilities of emerging technologies and big data analytics. To mitigate these issues, this paper proposes a Regional Computing (RC) paradigm for the management of HBD. The RC framework establishes strategically positioned regional servers capable of regionally collecting, processing, and storing medical data, thereby reducing dependence on centralized cloud resources, especially during peak usage periods. This innovative approach effectively addresses the constraints of traditional cloud processing, facilitating real-time data analysis at the regional level. Ultimately, it empowers healthcare providers with the timely information required to deliver data-driven, personalized care and optimize treatment strategies.

Efficient Workflow Scheduling in Cloud Computing Using Hybrid Algorithm

In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, considering deadlines and budgets while optimizing task allocation to virtual machines (VMs). Our study hypothesizes that improved scheduling can reduce energy consumption, streamline process execution, and lower operational costs. To test this hypothesis, we conducted a comparative analysis of two optimization techniques: Genetic Algorithm with Multiple Particle Swarm Optimization (GA + MPSO) and Genetic Algorithm with Bat Algorithm (GA + BAT). The analysis reveals that the combination of Genetic Algorithm and Bat Algorithm (GA + BAT) excels in optimizing cloud computing workflow scheduling. GA + BAT demonstrated superior performance by significantly reducing energy consumption, shortening process execution times, and decreasing operational costs. These findings validate our hypothesis, underscoring that optimizing cloud computing workflow scheduling can deliver substantial benefits. Consequently, by adopting GA + BAT, cloud service providers can enhance efficiency, reduce costs, and foster a more sustainable and responsive cloud infrastructure.

Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study.

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.

"Evaluation of the Scalability and Performance of the Open Source Serverless Computing Platforms"

The most powerful cloud computing service model currently being developed is the Serverless computing model. This model brings scalability and cost optimization in the process of deploying applications on cloud infrastructure. The application will be divided into functions that run a specific logic and those functions will be deployed as independent units on the Serverless computing platform. One of the strongest points of deploying applications running on the Serverless computing platform is its scalability and good processing performance. Scalability is demonstrated through the process of recognizing the actual usage needs of the function, from which the controllers in the Serverless computing platform will calculate and coordinate resources in the cloud environment appropriately, ensuring both service availability and saving idle resources. The performance of functions deployed on the Serverless computing platform mainly comes from the computing and storage capacity provided by the infrastructure, but the Serverless platforms also participate in supporting the process of optimizing the operating flow to minimize processing time and return response results to requests from the function caller. In this paper, we will build an experimental model with the two most popular open source Serverless computing platforms in the cloud computing development community, OpenFaaS and Knative. The purpose of this work is to compare and evaluate the scalability and performance in the process of operating applications on the open source Serverless computing platform. These two platforms rely on two different parameters to decide on the number of function instances.

A Hybrid Approach to Multi-Objective Task Scheduling in Cloud Computing: Merging Estimation of Distribution and Genetic Algorithms

In the evolving landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing performance. This paper presents a novel hybrid algorithm that combines the strengths of Estimation of Distribution Algorithms (EDA) and Genetic Algorithms (GA) to address multi-objective task scheduling challenges. The proposed approach aims to balance conflicting objectives such as minimizing execution time, energy consumption, and cost while maximizing resource utilization. By leveraging the probabilistic modeling capabilities of EDA and the evolutionary search efficiency of GA, the hybrid algorithm achieves a more diverse solution set and faster convergence compared to traditional methods. Extensive simulations demonstrate the effectiveness of the proposed algorithm across various cloud environments and task complexities. The results highlight significant improvements in achieving Pareto-optimal solutions, offering a robust framework for cloud service providers to enhance scheduling efficiency. This study underscores the potential of hybrid metaheuristic techniques in addressing the dynamic and complex nature of cloud computing task scheduling, paving the way for more resilient and adaptive cloud infrastructures. Keywords: Cloud Computing, Task Scheduling, Estimation of Distribution Algorithm, Genetic Algorithm, Multi-Objective Optimization, Load Balancing, Resource Utilization.

Cloud Resource Management and Cost Optimization

Cloud computing has emerged as a pivotal innovation, facilitating businesses in obtaining scalable, adaptable, and economical IT resources. Nonetheless, the intricate nature of cloud infrastructure management may result in unpredictable expenses if resources are not utilized efficiently. As organizations progressively shift their operations to cloud platforms, there is an increasing demand for effective resource management and strategies aimed at cost optimization to avert issues such as over-provisioning, underutilization, and decline in performance. This paper investigates essential strategies for optimizing cloud resource utilization, including auto-scaling, workload placement, and appropriate resource sizing. It also emphasizes cost-reduction methods like intelligent pricing models and dynamic resource allocation. By reviewing contemporary research and established best practices, this study highlights the challenges that businesses encounter when managing cloud resources and proposes solutions to reduce operational costs without compromising performance. Through case studies and empirical evaluations, this paper outlines a framework for proficient cloud resource management that aligns technical capabilities with organizational objectives. The outcomes aim to furnish organizations with practical insights on enhancing their cloud performance while minimizing expenses associated with cloud services. Keywords Cloud computing, resource management, cost optimization, auto-scaling, cloud services,

Enhancing Data Protection and Covert Communication in Cloud Environments with Isogeny Based Cryptography and Spread Spectrum Steganography

The rise of cloud computing has changed how individuals and organizations store and communicate data, but it additionally caused significant concerns about information security and confidentiality. In order to improve data protection and allow covert communication in cloud environments, this research suggests a unique approach that combines spread spectrum steganography with isogeny-based cryptography as a solution to these problems. The main goal is to make it less difficult for two users, User 1 and User 2, to communicate private data that is kept in a cloud infrastructure through safe and covert data transfer. This technology depends on isogeny-based cryptography, which creates safe channels of communication between users by taking use of the mathematical features of isogenies of elliptic curves. Additionally, this technique provides protection from quantum assaults, consequently boosting data security. Spread Spectrum Steganography (SSIS), the second element, is used to secretly include shared secrets inside digital images. To protect secrets, SSIS encrypts, duplicates, interleaves, and employs pseudorandom noise sequences as carriers. The noise in the stegoimage is then removed with a filter to approximate the original image. By performing this, the stegoimage is made integrate in with the original, thereby hiding the secrets. Users 1 and 2 can safely share information by combining spread spectrum steganography and isogeny-based cryptography, reducing potential risks associated with parties that are unreliable. Spread spectrum steganography provides the covert hiding of transmitted secrets inside digital images, while isogeny-based cryptography provides secure communication channels. These methods are combined to build an effective structure for private and secure data sharing in cloud settings. This novel method solves important concerns about data security, secrecy, and covert communication, and thus makes a significant addition to the developing field of cloud computing security.

Accelerated Cloud Infrastructure Development Using Terraform

Accelerated Cloud Infrastructure Development Using Terraform

An Effective Workload Prediction with Rnn-Lstm For Efficient Resource Autoscaling In Private Cloud Environments

The research focuses on an accurate workload prediction approach for auto-scaling resources in the Private Cloud using improved Time-Series models. Although many factors still result in dynamic workloads of cloud systems, an accurate forecast becomes vital for service quality and cost. The chapter discusses a Proactive Prediction Engine (PPE) framework using Auto Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network Long Short-Term, to forecast CPU utilization. Real-time datasets of OpenStack private cloud and Amazon AWS were used for experimental evaluation. The analyses show that the RNN_LSTM model performs far better than ARIMA by reducing the MAE and RMSE values by roughly 40 percent in each set. This has further reinforced that RNN_LSTM can model non-linearity and handle correlation issues in the workload data. Automated scaling of the instances with the Open Stack based on the predicted CPU load is made possible by the integration of RNN_LSTM prediction with OpenStack, supported by Terraform. This strategy reduces times of service outages and enables the efficient use of resources in the network. Regarding accuracy and automation, the proposed method can be a relevant solution for workload management for private cloud infrastructure. In this respect, the results support the implementation of deep learning-based predictive models to optimize the performance of autoscaling.

Implementing zero trust security models in cloud computing environments

In today’s rapidly evolving digital landscape, the adoption of cloud computing has fundamentally reshaped how organizations manage, store, and secure data. While cloud infrastructures offer scalability, flexibility, and resource efficiency, they also introduce complex security challenges. Traditional perimeter-based security models fall short in the face of these new threats, necessitating a paradigm shift towards the Zero Trust Security (ZTS) model. This model enforces strict access controls, continuous monitoring, and assumes no inherent trust within or outside the network. This paper offers an extensive exploration of Zero Trust in cloud computing environments, delving into its principles, architecture, implementation strategies, challenges, and anticipated future developments. Through a mix of theoretical discussion, technical frameworks, and real-world case studies, the study aims to provide a robust roadmap for organizations aspiring to adopt Zero Trust models in cloud infrastructures, achieving a balance between security and operational efficiency.

Artificial Intelligence in Optimization of Cloud Resources

Artificial intelligence (AI) is emerging as a transformative force in cloud product optimization, enabling organizations to achieve efficiency, scalability, and cost-effectiveness that were previously unattainable. The complexity of managing cloud resources, including cost, performance, and reliability, has increased dramatically with the widespread adoption of cloud computing. AI techniques, such as machine learning, deep learning, and reinforcement learning, can be leveraged to address these complexities by predicting workload patterns, automating resource allocation, and ensuring optimal performance through proactive monitoring and adjustments. This article provides an in-depth exploration of AI-based methods used for optimizing cloud infrastructure, focusing on real-world scenarios like dynamic resource allocation, pricing prediction, and service reliability. Additionally, we present the challenges of AI adoption in cloud optimization and outline potential directions for future research

RESEARCH AND FORECASTING OF TIME SERIES USING PARALLEL COMPUTING TECHNOLOGIES

This study addresses the challenge of efficient time series processing for forecasting purposes using distributed computing technologies in a cloud environment. The focus is placed on adapting modern approaches to time series analysis for handling large data volumes and integrating them with cloud computing infrastructure. Particular attention is given to processing ultra-long time series, characterized by low signal-to-noise ratios, complex structures, and long-term trends. A wide range of forecasting methods is analyzed, including classical statistical models such as autoregressive integrated moving average (ARIMA) and modern machine learning approaches, particularly long short-term memory neural networks. The advantages of parallel computing in significantly accelerating the processing of large data volumes are demonstrated. Specifically, the study confirms the effectiveness of the proposed approach using Amazon Web Services cloud infrastructure, enabling resource optimization and improving forecasting accuracy. A software package based on Apache Spark technologies was developed for time series analysis in distributed environments. Performance testing of the software demonstrated its practical applicability for solving forecasting and anomaly detection tasks in large time series. The application of the adapted autoregressive integrated moving average model, combined with parallel computing, is substantiated as an effective method for time series forecasting. The challenges associated with implementing parallel computing for time series forecasting are explored, including the need for algorithm optimization and ensuring scalability of solutions within a cloud environment. The study outlines prospects for further software enhancements, such as integrating adaptive algorithms and expanding their application to fields like cybersecurity, financial analytics, infrastructure monitoring, and forecasting in economics and industry. The results of extensive computational experiments confirm the effectiveness of the developed algorithms in improving forecast accuracy and reducing data processing time. These findings lay the foundation for future research aimed at creating comprehensive time series analysis systems that account for the specific needs of various industries.

Zero Trust Principles in Cloud Security: A DevOps Perspective

This research introduces Zero Trust Security models integrated into DevOps workflows. The paper outlines the implementation of security controls in GCP environments and evaluates their efficacy in mitigating risks associated with modern cloud infrastructures. The rise of cloud computing has revolutionized enterprise IT environments but has also introduced new security challenges. Zero Trust principles, which advocate "never trust, always verify," have emerged as a robust framework for securing cloud infrastructure. This paper explores the integration of Zero Trust principles within the DevOps lifecycle to enhance cloud security. It highlights how DevOps practices such as continuous integration/continuous delivery (CI/CD) and infrastructure as code (IaC) can align with Zero Trust methodologies to enforce granular access controls, continuous verification, and real-time threat detection. By embedding Zero Trust within the DevOps paradigm, organizations can establish a resilient security posture that adapts to evolving threats while maintaining operational agility.

AI-Powered Monitoring: Next-Generation Observability Solutions for Cloud Infrastructure

The paper proposes an advanced observability solution integrating AI-driven tools with Prometheus and Grafana for real-time system monitoring. It evaluates the impact on reducing downtime and improving predictive maintenance in cloud environments, marking a significant leap in operational efficiency.In the rapidly evolving landscape of cloud infrastructure, maintaining system reliability, performance, and scalability is more challenging than ever. Traditional monitoring approaches often struggle to keep pace with the dynamic and complex nature of modern cloud environments. This paper explores AI-powered monitoring, a transformative approach that leverages artificial intelligence and machine learning to deliver next-generation observability solutions. By analyzing vast amounts of data in real-time, AI-driven observability tools offer unparalleled insights into system behavior, enabling proactive detection of anomalies, predictive maintenance, and automated remediation. These innovations not only enhance system reliability but also empower organizations to optimize resource utilization and reduce operational costs. Through case studies and performance metrics, this paper demonstrates the critical role of AI in redefining monitoring strategies for cloud-native infrastructures, paving the way for a more resilient and efficient digital future.

AI-Driven Cloud Platform for Optimizing Short-Term Rental Property Furnishings

Abstract—Short-term rental platforms like Airbnb and VRBO have revolutionized the hospitality industry, creating a pressing need for efficient property setup tools tailored to diverse rental audiences. This paper introduces Hostly, an innovative AI-powered cloud platform that provides personalized recommendations for furnishing short- term rental properties. Built on Amazon Web Services (AWS), Hostly incorporates AI recommendation systems, real-time vendor integration, cost-ROI analytics, and secure payment processing. The platform simplifies property setup by curating tailored furnishing solutions, maximizing aesthetic appeal, functionality, and return on investment (ROI). Technical details of the AI architecture, cloud infrastructure, and payment processing pipelines are discussed. The paper also evaluates business impacts and future scalability Keywords—AI recommendation systems, AWS cloud services, Analytics dashboards, Cloud infrastructure, E- commerce, PCI DSS compliance, Payment processing, Short-term rental optimization, Vendor API integration.

Intelligent Cloud Systems: AI-Driven Enhancements in Scalability and Predictive Resource Management

This research article explores the transformative role of Artificial Intelligence (AI) in cloud computing, with a focus on its influence on scalability, resource management, and predictive analytics within distributed systems. As cloud infrastructure becomes integral to organizational operations, AI technologies have emerged as vital tools for enhancing performance and efficiency. The study emphasizes how AI enables scalability through dynamic resource allocation and auto-scaling, allowing systems to adapt seamlessly to changing demands. It also examines AI-driven resource management strategies, which utilize machine learning algorithms to optimize operations, lower costs, and ensure system resilience through predictive maintenance and anomaly detection. Furthermore, the article highlights predictive analytics, demonstrating how AI processes large datasets to support informed decision-making and improve system reliability. By integrating intelligent automation with cloud computing, this article underscores AI's pivotal role in advancing the capabilities of distributed systems

Design and Evaluation of a Cloud Computing System for Real-Time Measurements in Polarization-Independent Long-Range DAS Based on Coherent Detection.

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently.