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Articles de revues sur le sujet « Auto-Scaling policies »

Auteur : Grafiati

Publié le 1 juin 2024

Mis à jour le 31 juillet 2025

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1

Vemasani, Preetham, Sai Mahesh Vuppalapati, Suraj Modi, and Sivakumar Ponnusamy. "Achieving Agility through Auto-Scaling: Strategies for Dynamic Resource Allocation in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3169–77. http://dx.doi.org/10.22214/ijraset.2024.60566.

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Abstract: Auto-scaling is a crucial aspect of cloud computing, allowing for the efficient allocation of computational resources in response to immediate demand. This article delves into the concept of auto-scaling, its key components, and the strategies used to effectively manage resources in cloud environments. This study emphasizes the importance of auto-scaling in the cloud computing landscape by exploring its benefits, including cost efficiency, performance optimization, high availability, and scalability [1]. The article explores the various factors to consider when implementing scaling p

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Guo, Yuan Yuan, Jing Li, Xin Chun Liu, and Wei Wei Wang. "Batch Job Based Auto-Scaling System on Cloud Computing Platform." Advanced Materials Research 756-759 (September 2013): 2386–90. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2386.

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With the quick development of information science, it becomes much harder to deal with a large scale of data. In this case, cloud computing begins to become a hot topic as a new computing model because of its good scalability. It enables customers to acquire and release computing resources from and to the cloud computing service providers according to current workload. The scaling ability is achieved by system automatically according to auto scaling policies reserved by customers in advance, and it can greatly decrease users operating burden. In this paper, we proposed a new architecture of au

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viatoire, Dr T. Amalraj. "Dynamic Auto-Scaling and Load-Balanced Web Application Deployment in AWS." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49936.

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ABSTRACT Web applications must be fast, dependable, and able to manage evolving user needs without collapsing or becoming overly costly to maintain in the digital environment of today. Manual server management or traffic spike handling in traditional approaches of application deployment sometimes result in downtime, inadequate performance, or expensive costs. This project, "Dynamic Auto-Scaling and Load-Balanced Wed Application Deployment In AWS," thus emphasizes on creating a cloud-based infrastructure capable of automatically adjusting to demand, remain available, and operate effectively wit

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Rajput, R. S., Dinesh Goyal, Rashid Hussain, and Pratham Singh. "Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2430–34. http://dx.doi.org/10.1166/jctn.2020.8912.

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The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or termi

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Evangelidis, Alexandros, David Parker, and Rami Bahsoon. "Performance modelling and verification of cloud-based auto-scaling policies." Future Generation Computer Systems 87 (October 2018): 629–38. http://dx.doi.org/10.1016/j.future.2017.12.047.

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Researcher. "SCALING: THE BACKBONE OF EFFICIENT CLOUD PLATFORMS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 1–13. https://doi.org/10.5281/zenodo.13644708.

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This comprehensive article explores the concept of elastic scaling in cloud computing, detailing its significance, key components, applications, benefits, and challenges. It examines how elastic scaling enables dynamic resource allocation in response to fluctuating demands, optimizing performance and cost-efficiency. The article discusses various auto-scaling techniques, scaling policies, and the importance of monitoring and observability in implementing effective elastic scaling strategies. It highlights the wide-ranging applications of elastic scaling across different industries and use case

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A. Karunamurthy, Dr. "Scalable Web Application Deployment Using Auto Scaling, Load Balancer, And RDS." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49702.

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Abstract The rapid growth of web applications and the increasing demand for high availability, scalability, and performance have made traditional deployment methods inadequate. This project, titled "Scalable Web Application Deployment Using Auto Scaling, Load Balancer, and RDS," focuses on creating a robust and efficient infrastructure for deploying web applications on the cloud. The main goal is to ensure that the application can automatically adapt to changing workloads while maintaining optimal performance and availability. The proposed system utilizes Amazon Web Services (AWS) as the cloud

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Gudelli, Venkata Ramana. "Optimizing Elastic Kubernetes Services for High Availability Applications." Journal of Computational Intelligence and Robotics 1, no. 2 (2021): 64–88. https://doi.org/10.5281/zenodo.15102526.

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Cloud native architectures are adapted rapidly which makes the deployment highly available, resilient, and scalable applications on Kubernetes. Elastic Kubernetes Services (EKS) provides an automated managed environment that facilitates dynamic resource allocation and workload distribution. But optimization of EKS for high availability requires precise tuning of cluster configurations, node auto-scaling policies, and fault tolerance mechanisms. The purpose of this paper is to examine the key architectural components which influences the EKS performance which includes pod disruption budgets, ho

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A. Karunamurthy, Dr. "SECURE AND SCALABLE WORDPRES DEPLOYMENT ON AWS WITH RDS." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04077.

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Abstract This paper digital landscape, ensuring high availability, scalability, and security for web applications is crucial. This project focuses on deploying a secure and scalable WordPress website on Amazon Web Services (AWS) using industry best practices. By leveraging AWS services such as EC2, Auto Scaling, RDS, S3, VPC, IAM, and Security Groups, this deployment achieves high performance, reliability, and security. The architecture includes Amazon EC2 instances running WordPress in an Auto Scaling Group, ensuring seamless horizontal scalability. Amazon RDS is used for the MySQL database,

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Owoade, Samuel, Abraham Ayodeji Abayomi, Abel Chukwuemeke Uzoka, Oyejide Timothy Odofin, Oluwasanmi Segun Adanigbo, and Jeffrey Chidera Ogeawuchi. "Predictive Infrastructure Scaling in Fintech Systems Using AI-Driven Load Balancing Models." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2393–401. https://doi.org/10.62225/2583049x.2024.4.6.4356.

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As the fintech industry continues to experience exponential growth, the ability to scale infrastructure dynamically and efficiently has become a critical concern. Traditional methods of load balancing and resource scaling often fail to meet the demands of real-time fintech applications, resulting in performance bottlenecks, high operational costs, and system inefficiencies. This paper explores the integration of artificial intelligence (AI) in predictive infrastructure scaling to address these challenges. Specifically, it investigates AI-driven models, including machine learning algorithms suc

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Wei, Yi, Daniel Kudenko, Shijun Liu, Li Pan, Lei Wu, and Xiangxu Meng. "A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment." Mathematical Problems in Engineering 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/5080647.

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Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers

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Bhattacharjee, Brijit, Bikash Debnath, Jadav Chandra Das, et al. "Predicting the Future Appearances of Lost Children for Information Forensics with Adaptive Discriminator-Based FLM GAN." Mathematics 11, no. 6 (2023): 1345. http://dx.doi.org/10.3390/math11061345.

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This article proposes an adaptive discriminator-based GAN (generative adversarial network) model architecture with different scaling and augmentation policies to investigate and identify the cases of lost children even after several years (as human facial morphology changes after specific years). Uniform probability distribution with combined random and auto augmentation techniques to generate the future appearance of lost children’s faces are analyzed. X-flip and rotation are applied periodically during the pixel blitting to improve pixel-level accuracy. With an anisotropic scaling, the image

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Bhargavi, K., and B. Sathish Babu. "Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach." Cybernetics and Information Technologies 19, no. 3 (2019): 94–117. http://dx.doi.org/10.2478/cait-2019-0028.

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Abstract Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application

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Oyekunle Claudius Oyeniran, Adebunmi Okechukwu Adewusi, Adams Gbolahan Adeleke, Lucy Anthony Akwawa, and Chidimma Francisca Azubuko. "Microservices architecture in cloud-native applications: Design patterns and scalability." Computer Science & IT Research Journal 5, no. 9 (2024): 2107–24. http://dx.doi.org/10.51594/csitrj.v5i9.1554.

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Microservices architecture has emerged as a pivotal approach for designing scalable and maintainable cloud-native applications. Unlike traditional monolithic architectures, microservices decompose applications into small, independently deployable services that communicate through well-defined APIs. This architectural shift enhances modularity, allowing for improved scalability, resilience, and flexibility. This paper explores the core concepts of microservices, including service decomposition, inter-service communication, and data management. It delves into key design patterns such as the API

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Parag, Bhardwaj. "Automating Cost Optimization with Azure Monitor and Log Analytics." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 6 (2021): 1–10. https://doi.org/10.5281/zenodo.14593207.

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Automation plays a crucial role in cloud cost management and optimization by enabling organizations to streamline their processes, reduce human error, and achieve more efficient resource utilization. Cloud environments are dynamic and scalable, which means that manual cost monitoring and optimization can be time-consuming, error-prone, and ineffective in addressing fluctuating demand and usage patterns. By automating cost management tasks, such as resource scaling, rightsizing, and deallocation of unused resources, businesses can significantly reduce waste and ensure resources are used only wh

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Russo Russo, Gabriele, Valeria Cardellini, and Francesco Lo Presti. "Hierarchical Auto-Scaling Policies for Data Stream Processing on Heterogeneous Resources." ACM Transactions on Autonomous and Adaptive Systems, May 16, 2023. http://dx.doi.org/10.1145/3597435.

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Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism at run-time. However, most the effort has been spent under the assumption of homogeneous computing infrastructures, neglecting the complexity of modern environments. We consider the problem of

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Tournaire, Thomas, Hind Castel-Taleb, and Emmanuel Hyon. "Efficient Computation of Optimal Thresholds in Cloud Auto-Scaling Systems." ACM Transactions on Modeling and Performance Evaluation of Computing Systems, June 6, 2023. http://dx.doi.org/10.1145/3603532.

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We consider a horizontal and dynamic auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off in order to minimise energy consumption while meeting performance requirements. Finding cloud management policies that adapt the system to the load is not straightforward and we consider here that virtual machines are turned on and off depending on queue load thresholds. We want to compute the optimal threshold values that minimize consumption costs and penalty costs (when performance requirements are not met). To solve this problem, we propose se

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Didona, Diego, Paolo Romano, Sebastiano Peluso, and Francesco Quaglia. "Transactional Auto Scaler." July 11, 2014. https://doi.org/10.1145/2620001.

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In this article, we introduce TAS (Transactional Auto Scaler), a system for automating the elastic scaling of replicated in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from online self-optimization of in-production applications to the automatic generation of QoS/cost-driven elastic scaling policies, as well as to support for what-if analysis on the scalability of transactional applications. In this article, we present the key innovation at the core of TAS, namely, a novel performance forecasting methodology that re

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Jorge, Ortín, Serrano Pablo, Garcia-Reinoso Jaime, and Banchs Albert. "Analysis of scaling policies for NFV providing 5G/6G reliability levels with fallible servers." IEEE Transactions on Network and Service Management, January 25, 2022. https://doi.org/10.1109/TNSM.2022.3147146.

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The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficien

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-, Srikanth Jonnakuti. "Adaptive Reinforcement Learning for Dynamic Resource Allocation in Cloud Data Pipelines." International Journal of Leading Research Publication 4, no. 2 (2023). https://doi.org/10.70528/ijlrp.v4.i2.1556.

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The explosive growth in data-driven applications has increased the need for real-time analytics of data, requiring extremely efficient and scalable resource provisioning within cloud and edge computing setups. Conventional resource allocation methods do not effectively respond to changing workloads, leading to wastage of resources or decline in performance. This work introduces a reinforcement learning (RL)-driven auto-scaling framework tailored for streaming analytics platforms with an emphasis on optimizing ETL and inference clusters. Using deep and multi-agent RL agents, the system learns a

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