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Journal articles on the topic 'AWS CloudFormation'
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Prudhveer, Reddy Kankar. "Automated Cleanup of Unused AWS Cloud Formation Resources Using AWS Resource Tags and Lambda Functions." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 2563–64. https://doi.org/10.5281/zenodo.14885967.
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The AWS cloud platform has experienced rapid growth due to its expanding features and support for on-demand access to compute, storage, networking, and virtualization. AWS CloudFormation is a service that enables developers and busi- nesses to create, provision, and manage a collection of related AWS and third-party resources in an orderly and predictable manner. AWS CodePipeline can be utilized to deploy AWS CloudFormation stacks, enhancing continuous integration and continuous delivery (CI/CD) capabilities. Companies often use multiple AWS accounts for different environments and deploy resources across them. As part of the CI/CD process, a central AWS account is used to deploy CloudFormation stacks to other accounts using AWS CodePipeline. However, when an application is no longer needed, there is no straightforward way to use the centralized account to delete the unused CloudFormation resources. While CloudFormation stacks can be updated and modified from the central account using AWS CodePipeline, deleting them remains a challenge. This paper discusses an approach to address this issue using CloudFormation tags and AWS Lambda.
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Raju, Dachepally. "Implementing Infrastructure as Code (IaC) with AWS CloudFormation." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 1 (2025): 1–5. https://doi.org/10.5281/zenodo.14866453.
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Infrastructure as Code (IaC) has revolutionized cloud resource management by enabling automation, consistency, and scalability. AWS CloudFormation is a powerful IaC tool that automates the provisioning of cloud infrastructure using templates. This paper explores the architecture, implementation strategies, best practices, and challenges associated with AWS CloudFormation. It includes real-world case studies, pseudocode examples, and visual representations such as flowcharts and graphs to illustrate the benefits of CloudFormation over manual infrastructure management. The paper also discusses cost savings, security compliance, and future trends in IaC adoption.
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Patibandla, Kondala Rao. "Automate Amazon Aurora Global Database Using Cloud Formation." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 262–70. http://dx.doi.org/10.60087/jaigs.v2i1.209.
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This article provides an in-depth guide on deploying an AWS Aurora Global Database using AWS CloudFormation. It covers the benefits of Aurora Global Databases, such as high availability, low-latency global reads, and disaster recovery capabilities. The article details the CloudFormation template structure and key parameters needed to set up the Aurora Global Database, enabling seamless data replication across multiple AWS regions. Practical examples and best practices are included to ensure a robust and efficient deployment process.
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Koneru, Naga Murali Krishna. "Infrastructure as Code (IaC) for Enterprise Applications: A Comparative Study of Terraform and CloudFormation." American Journal of Technology 4, no. 1 (2025): 1–29. https://doi.org/10.58425/ajt.v4i1.351.
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Aim: The objective of this study was to evaluate two tools within this category, namely Terraform and AWS CloudFormation and compare their suitability for managing enterprise cloud infrastructure under Infrastructure as Code (IaC) principles. Methods: Using a comparative evaluation method based on feature analysis, use case modeling, and expert interpretation. The research evaluates these criteria through syntactic usability, state management, modularity, CI/CD integration, security practices, policy enforcement, and deployment performance. Results: HashiCorp product Terraform is a new entry to the IaC world. It is a provider‑agnostic tool famous for its flexible template structure and support of multi‑cloud environments such as AWS, Azure, and Google Cloud. It provides strong flexibility, very reusable modules, and has a robust open-source ecosystem. Conversely, AWS CloudFormation is tightly integrated with AWS services and supports compliance, orchestration, and automation of AWS-centric environments through JSON/YAML templates, StackSets, and IAM policy integration. The analysis points to Terraform as an option for enterprises moving towards hybrid or multi-cloud strategies, given its high mark in modularity, ecosystem breadth, and cross-platform deployment. However, CloudFormation is superior in aligning compliance, safety in operations, and governance, particularly for AWS exclusive infrastructures. Conclusion: The study concludes that with the right IaC tool, enterprises can scale their infrastructure appropriately, comply with requirements, and quickly deploy infrastructures in an automated and rapid manner. Recommendations: If organizations want to have the most portable and flexible configuration across platforms, they should choose Terraform. In contrast, if they desire the simplest integration with AWS services in a regulated environment, they should instead pick CloudFormation.
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Praveen, Kumar Koppanati. "Disaster Recovery and Business Continuity for P&C Insurance Systems Using AWS CloudFormation and Elastic Disaster Recovery." Journal of Scientific and Engineering Research 11, no. 10 (2024): 1–7. https://doi.org/10.5281/zenodo.13912415.
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Disaster recovery and business continuity have become critical considerations in the rapidly evolving digital infrastructure of Property and Casualty (P&C) insurance systems. As more organizations shift their core systems to the cloud, the necessity for efficient, scalable, and cost-effective disaster recovery strategies has increased. This paper explores how Amazon Web Services (AWS) CloudFormation and Elastic Disaster Recovery (DRS) provide robust disaster recovery and business continuity solutions for P&C insurance systems. By leveraging Infrastructure as Code (IaC) through AWS CloudFormation, P&C insurers can automate the deployment and scaling of recovery infrastructure, reducing downtime and maintaining data integrity during a disaster. AWS Elastic Disaster Recovery offers seamless replication, rapid failover, and cross-region recovery capabilities that ensure business continuity even in the most critical scenarios. This paper also analyzes how the AWS Well-Architected Framework aligns with these technologies, ensuring operational excellence, security, and performance efficiency. Case studies from leading insurance companies are reviewed to demonstrate real-world implementations and the value of adopting cloud-based disaster recovery strategies.
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Bollineni, Satyadeepak. "A Practical Roadmap for Enterprise Systems to Streamline Cloud Migration Approaches." International Scientific Journal of Engineering and Management 01, no. 01 (2022): 1–7. https://doi.org/10.55041/isjem00108.
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Organizations now see cloud migration as their main strategic approach to obtain scalability while simultaneously lowering their operational costs and boosting system performance. This paper outlines a hands-on guide for infrastructure migration from traditional sites to AWS systems which emphasizes best practices together with automated solutions and security developments as well as cost-saving strategies. It describes important migration steps which includes beginning with creating migration runbooks and building secured AWS AMIs and then using VPC EC2 ELB and CloudWatch services and ending with automated deployment with CloudFormation and Ansible. It covers security requirements and provides strategies to reduce downtime events. In this paperI will review common challenges enterprises face while migrating along with data integrity challenges network complexities and compliance. Keywords—Cloud Migration, AWS Migration, Enterprise Cloud Adoption, Migration Runbook, Secure Amazon Machine Images (AMIs), Log Analytics, Cloud Automation, CloudFormation and Ansible, Security Best Practices, IAM Policies and Data Encryption.
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Chirumamilla, Sai Krishna. "From Code to Cloud: Automating Continuous Deployment with AWS Cloud Development Kit (CDK) and Infrastructure as Code (IaC)." International Scientific Journal of Engineering and Management 03, no. 05 (2024): 1–7. https://doi.org/10.55041/isjem01653.
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Abstract: Infrastructure as Code or IaC is now an optimal method for managing resources in the cloud as the flexible, automated, and versioned approach for DevOps. As cloud services become more popular, such as the AWS Cloud Development Kit, CDK enables developers to leverage their favorite programming languages for infrastructure provisioning. The extension of IaC is the AWS CDK, which allows for the management of infrastructure in higher-level constructs in TypeScript, Python, and other programming languages. It can easily be implemented in modern CI/CD pipelines. This paper discusses the IaC with AWS CDK and continuous deployment pipelines, which extend the solution for deployment automation. The three key priorities mentioned above include creating reusable artifacts, refining the build processes, and adopting GitHub Actions and AWS CodePipeline as key tools for delivery mechanisms. As such, we offer practical recommendations for CDK, discuss the benefits of using the AWS CDK over a template-based IaC tool such as AWS CloudFormation, and detail how AWS CDK minimizes infrastructure drift. The paper also discusses the goals and problems of CDK in regard to automated deployment, including security, scalability, and monitoring issues or quick wins. AWS CDK constructs are used in production-grade Multi-Account setups, and real-world multi-account examples are included to showcase the tool. Keywords: AWS Cloud Development Kit (CDK), Infrastructure as Code (IaC), Automation, DevOps, Cloud Computing, AWS CodePipeline, GitHub Actions, AWS CloudFormation, CI/CD Pipelines.
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Kumar, Nagresh, and Sanjay Kumar Sharma. "A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 144–49. http://dx.doi.org/10.37391/ijeer.100216.
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Recent technological advancements in the IT field have pushed many products and technologies into the cloud. In the present scenario, the cloud service providers mainly focus on the delivery of IT services and technologies rather than throughput. In this research paper, we used a scalable cost-effective approach to configure AWS Batch with AWS Fargate and CloudFormation and implemented it in order to handle a heavy workload. The AWS service configuration procedure, GitHub repository, and Docker desktop applications have been clearly described in this work. A cost-effective configuration and architecture of AWS Batch processing are given to provide high throughput. The processing of heavy workload by AWS Batch is represented in terms of execution time and the result shows that the concurrent execution reduces the execution time. To enhance the throughput heavy workload using batch processing an "Amazone FSx for Lustre" can also be used.
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Vijaya Kumar Katta. "Leveraging AWS cloud native services for scalable application architectures." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 2108–20. https://doi.org/10.30574/wjarr.2025.26.2.1853.
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AWS cloud-native services enable organizations to build scalable and resilient applications in today's transformed application development landscape. AWS has pioneered technologies that have become cornerstones of modern application architecture, offering comprehensive tools for implementing sophisticated solutions. The document examines serverless computing paradigms through AWS Lambda and API Gateway, highlighting their evolution, features, and best practices for implementation. It delves into container orchestration with Amazon ECS and EKS, comparing their capabilities and introducing Fargate as a serverless container execution option. Purpose-built database services including DynamoDB, Aurora Serverless, and ElastiCache are discussed alongside storage solutions like S3, EFS, and FSx, with emphasis on appropriate data access patterns and optimization techniques. Infrastructure automation through CloudFormation and CDK is explored, alongside continuous integration and deployment pipelines that form the foundation of modern software development practices. The examination of observability and monitoring tools essential for operating cloud-native systems effectively provides a comprehensive guide to leveraging AWS services for scalable application architectures
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Guduru, Sandhya. "AUTOMATED DISASTER RECOVERY ORCHESTRATION LEVERAGING TERRAFORM, ANSIBLE, AND AWS CLOUDFORMATION FOR RPORTO OPTIMIZATION." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY 10, no. 4 (2019): 355–65. https://doi.org/10.34218/ijaret_10_04_041.
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Miroshnychenko, Dmytro, and Olena Tolstoluzka. "EVALUATION OF THE EFFECTIVENESS OF THE “INFRASTRUCTURE AS CODE” METHODOLOGY FOR CREATING AND MANAGING CLOUD INFRASTRUCTURE." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (13) (July 11, 2025): 95–100. https://doi.org/10.20998/2079-0023.2025.01.14.
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The article describes a comprehensive study of the effectiveness of using the Infrastructure as Code (IaC) methodology to create, scale, and manage cloud infrastructure. The IaC methodology is considered one of the key technologies of digital transformation and the DevOps approach, which provides software automation of infrastructure processes, reduces dependence on the human factor, and increases the repeatability and predictability of IT environments. The article provides a comparative analysis of leading IaC implementation tools, in particular Terraform, Pulumi, AWS CloudFormation, and Ansible, from the standpoint of their openness, compatibility with various cloud platforms, architectural approach (declarative or imperative), state management, and level of flexibility. The degree of automation, scalability, speed of infrastructure deployment, adaptability to change, configuration reliability, and ease of management are evaluated as key performance metrics. For each metric, a theoretical justification, analytical assessment, and comparison with traditional approaches to administration are provided. Special attention is paid to the analysis of IaC implementation in leading cloud environments (AWS, Microsoft Azure, Google Cloud Platform, OpenStack) taking into account the corresponding platform solutions (CloudFormation, ARM/Bicep, Deployment Manager, Heat) and third-party multi-cloud tools. It was found that the use of IaC significantly improves DevOps practices, simplifies CI/CD processes and increases the reliability of cloud solutions. As a result, it is proven that the use of IaC provides a significant increase in operational efficiency, reduces infrastructure maintenance costs and promotes its standardization, which makes this methodology strategically important for modern IT systems.
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Vinisha Manoju, Likhita Konakalla, Maneesha Vedantham, and C Praveen Kumar. "Cinematic Cloud: Implementing a Video Streaming Platform with Containerization, Infrastructure as Code, And CI/CD Data Pipelines." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 10 (2024): 2529–37. http://dx.doi.org/10.47392/irjaeh.2024.0347.
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In today's digital landscape, video streaming platforms have become the cornerstone of entertainment consumption, reshaping how we engage with media. However, traditional approaches to building these platforms, often relying on monolithic architectures, are struggling to keep pace with the evolving demands of users and the complexities of modern infrastructure. This solution explores a new approach that uses contemporary methods like DevOps and microservices to make video streaming platforms faster, more reliable, and easier to use. The system starts by providing a user interface for streaming content and its entire backend architecture is built using specialized tools and methodologies. The methodologies include containerization using Docker and automation of IT infrastructure using Terraform or AWS CloudFormation which serve as Infrastructure as Code. Lastly, CI/CD pipelines are used to automate all stages of the SDLC life cycle. The application will be deployed on AWS cloud and there will be extensive use of AWS services.
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Manukonda, Anil Kumar. "Implementing Multi-Region Disaster Recovery Solutions in AWS Cloud Environment." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 06, no. 09 (2022): 1–8. https://doi.org/10.55041/ijsrem16417.
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Cloud computing depends on multi-region disaster recovery (DR) as a vital practice to maintain business operations during substantial outages. Organizations using Amazon Web Services (AWS) worldwide infrastructure implement disaster recovery through duplicated critical systems that span different geographical areas to minimize data loss and downtime. AWS users can establish multi-region DR strategies which this paper examines through specific implementations targeting e-commerce operations and healthcare as well as financial institutions. This paper explicates multi-region DR strategies including Recovery Time Objective and Recovery Point Objective followed by AWS implementation approaches with Amazon Route 53 along with AWS Lambda and Amazon DynamoDB global tables and AWS CodePipeline and AWS CloudFormation among other services that support effective multi- region DR. The paper demonstrates how a failover capable Route 53 DNS system pairs with DynamoDB global data replication for an active/passive deployment example. The text explores both the difficulties which include expenses and complexity alongside poor data coherence and inadequate testing methods and offers effective guidelines for multi-regional DR including routine disaster recovery exercises and similar configuration deployment between areas in addition to automated processes to minimize mishaps. The evidence confirms that AWS multi-region DR configurations following AWS Well-Architected Framework standards deliver dependable resilience and continuation to crucial business operations in any business sector.
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Satish Kumar Nalluri and Varun Teja Bathini. "Integrating Jenkins and AWS in Siemens Opcenter MES: Bridging the digital divide through modern development and deployment pipelines." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2064–74. https://doi.org/10.30574/wjaets.2025.15.2.0767.
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The integration of Jenkins and Amazon Web Services (AWS) into Siemens Opcenter MES (Manufacturing Execution System) represents a transformative approach to modernizing industrial software deployment. As manufacturing systems evolve toward Industry 4.0, the need for agile, scalable, and automated deployment pipelines becomes critical. This paper explores how Continuous Integration and Continuous Deployment (CI/CD) methodologies, facilitated by Jenkins automation and AWS cloud infrastructure, can bridge the digital divide in traditional MES environments. We present a structured framework for automating build, test, and deployment processes in Siemens Opcenter, leveraging AWS services (EC2, ECS, Lambda, CloudFormation) for scalable and secure execution. Key benefits include reduced deployment times, enhanced reliability, and cost-efficient scalability, while addressing challenges such as security, compliance, and system dependencies. A real-world case study demonstrates measurable improvements in deployment speed and operational efficiency, validating the proposed approach. Finally, we discuss emerging trends, including AI-driven deployment optimization, serverless architectures, and edge computing, highlighting future directions for MES DevOps. This research provides actionable insights for manufacturing IT teams seeking to adopt modern CI/CD pipelines while maintaining robustness in industrial environments.
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Mounika, Kothapalli. "Automated Infrastructure Provisioning and Management with Infrastructure as Code (IaC) Tools." Journal of Scientific and Engineering Research 7, no. 6 (2020): 279–84. https://doi.org/10.5281/zenodo.13337967.
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<strong> </strong>This paper reviews infrastructure as code tools and best practices with respect to the existing three major solution options: Terraform, Azure Resource Manager Template, and AWS CloudFormation. In this paper, the most important features, capabilities, and use cases of each tool are explained, and then a minute comparison with respect to each of them will be performed in view of parameters such as ease of use, flexibility, performance, cost implications, and integration capabilities. Best practices for version control, collaboration, and continuous integration/continuous deployment in IaC environments are also taken into consideration in the paper. With these objectives in mind, this research aims to provide insights useful for any organization plotted on the roadmap to either plan or improve their Infrastructure as Code practice for enhancing their capabilities in cloud infrastructure management. It concludes the discussion with the emerging trends in IaC and some practical recommendations while adopting these technologies for organizations.
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Nagaraju, Islavath. "Leveraging AWS for Scalable and Secure DevOps: A Comprehensive Guide to Best Practices." European Journal of Advances in Engineering and Technology 11, no. 1 (2024): 84–89. https://doi.org/10.5281/zenodo.13912628.
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In the modern technological world, DevOps is being adopted by organizations to increase the integration of development and operation teams to improve and speed up software delivery. However, IT security has become embedded into these fast-paced development and deployment cycles – known as DevSecOps - as a major challenge. AWS has numerous capabilities that enable a reliable and secure DevOps business environment that supports CI/CD practices. In the case of DevOps, one of the best practices seen in using the AWS environment is applying infrastructure as code. Applications such as AWS CloudFormation help teams codify their infrastructure to use version control for their resources; this cuts out human interference and enhances security. AWS Identity & Access Management (IAM) provides users and apps the most restrictive access they require to complete their activities, thereby reducing risk. Automation tools for security are another critical practice required in the pipeline of CI/CD. Some services, such as AWS CodePipeline, allow security checks at different levels, and yes, code is scanned for vulnerabilities before release. In addition, AWS CloudTrail and Amazon GuardDuty allow us to analyze continually, including any new threats and security risks, in real-time. The approach outlines how security can be incorporated into the SDLC to achieve high-risk management and regulatory compliance. This is not just about improving security strategies or attaining defensive objectives but about implementing 'good operational security practice,' thus allowing organizations to release secure and dependable software on a massive scale.
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Okafor, Kennedy Chinedu, Wisdom Onyema Okafor, Omowunmi Mary Longe, Ikechukwu Ignatius Ayogu, Kelvin Anoh, and Bamidele Adebisi. "Scalable Container-Based Time Synchronization for Smart Grid Data Center Networks." Technologies 13, no. 3 (2025): 105. https://doi.org/10.3390/technologies13030105.
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The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation.
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Bollineni, Satyadeepak. "Integrating DevOps for Efficient Infrastructure Management on Amazon web Services using AWS CloudFormation and Lambda." International Journal of Core Engineering & Management 6, no. 9 (2020): 66–75. https://doi.org/10.5281/zenodo.14935456.
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Chirumamilla, Sai Krishna. "From Monolith to Microservices: A Software Engineer’s Guide to Refactoring with AWS Technologies." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 06, no. 06 (2022): 1–15. https://doi.org/10.55041/ijsrem14595.
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The transition from monolithic architectures to microservices is an architectural change in the software engineering paradigm. This transformation enables the scalability, independence, and elasticity of the structures of applications. This paper seeks to bring into perspective a step-by-step procedure that will guide software engineers when refactoring from a monolithic architecture to microservices using AWS. The abstract starts by stating the problems that happen with monolithic systems, for instance, difficulty in scalability and managing its codebase, and produced suboptimal productivity of the developers. It then moves to the advantages of microservices, which include the ability to deploy individually, scale independently, and have better fault tolerance. This paper also highlights AWS services like Amazon ECS, AWS Lambda, and Amazon API Gateway, which help integrate and deploy microservices effectively. AWS CloudFormation and AWS X-Ray are investigated as to their positions in the infrastructural and visibility aspects, respectively. Here, emphasis is on the designs and migrations, what is best practice, and practice hazards that people face during the refactoring activity. This work is based on information about cloud-native design practices and examples of companies’ experience in using AWS to transform the architecture of software solutions. Some measurable indicators of migration success are defined as the number of deployments per time interval, lead time for change, Mean Time to Recover (MTTR), and microservices scalability. This paper employs activities and tasks in structured methodologies, flow charts, listed descriptions, and statistical analysis in order to arrive at a set of recommendations. Finally, the strategy considerations, low-level recommendations, and the prospect of micro-service-based architecture in the context of cloud computing are summarized. Keywords: Microservices, Monolith, AWS Technologies, Scalability, Deployment, Amazon ECS, AWS Lambda, API Gateway.
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Dasari, Hari. "Infrastructure as Code (IaC) Best Practices for Multi-Cloud Deployments in Enterprises." International journal of networks and security 05, no. 01 (2025): 174–86. https://doi.org/10.55640/ijns-05-01-10.
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As businesses increasingly adopt multi-cloud strategies to improve cost, performance, and availability, managing dispersed infrastructure across many providers becomes a crucial challenge. Infrastructure as Code (IaC) emerges as a key paradigm, allowing for automation, version control, and consistency in infrastructure provisioning and administration. This article provides a complete examination of IaC best practices for multi-cloud settings, focusing on modular architecture, tool standardization, governance, security integration, and automation via CI/CD pipelines. Terraform, AWS CloudFormation, and policy-as-code frameworks like OPA are all appraised for their use in cross-cloud orchestration. The paper uses case studies and practical examples to demonstrate how firms can streamline deployments, decrease operational risk, and assure regulatory compliance in complex enterprise systems. These insights are intended to assist DevOps and cloud engineering teams in creating durable, scalable, and secure multi-cloud infrastructures.
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Karthik Reddy Mannem. "Demystifying Infrastructure as Code (IAC): A practical guide for DevOps professionals." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 902–11. https://doi.org/10.30574/wjaets.2025.15.2.0580.
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This comprehensive article explores Infrastructure as Code (IAC), a transformative approach for DevOps professionals managing cloud infrastructure. The article examines how IAC enables organizations to treat infrastructure configuration as software, resulting in more consistent, repeatable, and maintainable cloud environments. It provides an in-depth analysis of key benefits including consistency across environments, version control capabilities, reduced human error, and improved documentation. The article evaluates popular IAC tools including Terraform, Azure Bicep, AWS CloudFormation, and Pulu mi, examining their distinct advantages for different organizational contexts. Best practices such as modularization, parameterization, remote state management, and immutable infrastructure are thoroughly explored, alongside practical implementation strategies for organizations at any stage of adoption. The article also addresses critical security considerations including the least privilege principle, secrets management, and policy as code implementation. Drawing on extensive research and industry reports, this article offers DevOps professionals a practical roadmap for successfully implementing IAC while navigating common challenges and leveraging emerging trends.
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Satyadeepak, Bollineni. "Implementing Infrastructure as Code (IaC) in Data Engineering: Benefits and challenges." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 8, no. 6 (2022): 1–7. https://doi.org/10.5281/zenodo.13949347.
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Infrastructure as Code has revolutionized how infrastructure operates and is automated, especially in environments such as cloud and DevOps. Infrastructure as Code finds its critical role in data engineering: a means of automating the setup of data pipelines, storage systems, and processing frameworks. This paper discusses a few benefits and some challenges that come with using IaC in data engineering environments. First, the key advantages are automation, scalability, cost-effectiveness, speedier deployment, and enhanced collaboration. Challenges include, but are not limited to, complexity, the learning curve, security concerns, and state management. IaC popular tools presented include Terraform, AWS CloudFormation, and Ansible; best practices to be used in the face of obstacles include modular design and Continuous Integration. The case studies represent real-world application of the IaC practice and provide at least some insight into the development trends yet to come, such as integration with Artificial Intelligence/Machine Learning and Edge computing. Finally, practical recommendations are given that may support data engineers who would like to take advantage of Infrastructure as Code and operate more efficient, scalable infrastructure.
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Sandhya, Guduru. "Automated Vulnerability Scanning & Runtime Protection for DockerKubernetes: Integrating Trivy, Falco, and OPA." Journal of Scientific and Engineering Research 6, no. 2 (2019): 216–20. https://doi.org/10.5281/zenodo.15234550.
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Securing Docker and Kubernetes environments is a critical challenge. Automated vulnerability scanning and runtime protection are essential to mitigate security risks while maintaining performance and compliance. Tools like Trivy, Falco, and Open Policy Agent (OPA) provide a powerful, automated security framework for detecting vulnerabilities, monitoring runtime behavior, and enforcing security policies in Kubernetes environments. This paper explores the security challenges inherent in containerized deployments, highlighting common vulnerabilities, compliance gaps, and runtime threats. It evaluates the role of Trivy for continuous vulnerability scanning, Falco for real-time threat detection, and OPA for policy enforcement within Kubernetes clusters. Additionally, the study assesses infrastructure-as-code (IaC) frameworks such as Terraform for state management, Ansible for automated recovery, and AWS CloudFormation for disaster recovery automation. The integration of Chaos Engineering tools like Gremlin enables testing of recovery point objectives (RPO) and recovery time objectives (RTO) under real-world failure conditions, while real-time replication technologies like DRBD and Ceph enhance system resilience. We propose a comprehensive security framework that integrates these tools to create a robust, automated security posture for Kubernetes environments.
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Koptsev, Oleg, Vitalii Martovytskyi, Nataliia Bolohova та Ilko Fedak. "ОСОБЛИВОСТІ АВТОМАТИЧНОГО РОЗГОРТАННЯ ІНФРАСТРУКТУРИ ЯК КОДУ ДЛЯ ХМАРНИХ СЕРВІСІВ". Системи управління, навігації та зв’язку. Збірник наукових праць 1, № 75 (2024): 104–8. http://dx.doi.org/10.26906/sunz.2024.1.104.
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Хмарні сервіси надають сучасні обчислювальні ресурси, доступні на вимогу через Інтернет. Завдяки хмарним обчисленням команди стають більш ефективними та скорочують час виходу на ринок, оскільки вони можуть швидко набувати та масштабувати послуги без значних зусиль, які потребує управління традиційною інфраструктурою. Автоматизація дозволяє командам покращувати ключові показники. Команди відмовляються від тривалих процесів, пов'язаних із внесенням змін та запланованими розгортаннями. Вони також переходять від реактивного виявлення проблем до запобіжного моніторингу та забезпечення прозорості. Мета статті – дослідити популярні засоби для реалізації інфраструктури як коду, що включають Terraform, AWS CloudFormation, ARM Templates, Ansible, Puppet, Chef та інші. Ці інструменти допомагають створювати, керувати та відстежувати інфраструктурні ресурси через програмний код. Використання автоматизованих практик IaC дозволить зберегти час, зменшити ризики, підвищити сумісність та спростити процеси розгортання та управління інфраструктурою. Розглянувши популярні засоби для реалізації інфраструктури як коду, що допомагають створювати, керувати та відстежувати інфраструктурні ресурси через програмний код, ми дійшли висновку, що Bicep дозволяє більш ефективно та зрозуміло працювати з розгортанням інфраструктури в Azure, а також полегшує роботу з ARM Templates. Використання Bicep, у порівнянні з ARM шаблонами та іншими інструментами IaC, дає можливість створювати скрипти, які є значно компактнішими за розміром. Це досягається завдяки більш лаконічному та зрозумілому синтаксису Bicep, що дозволяє описувати однакові набори ресурсів меншою кількістю коду. Такий підхід не тільки спрощує розробку та підтримку інфраструктурного коду, але й знижує поріг входження для нових користувачів, які мають досвід роботи з програмуванням.
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Uddesh, Piprewar, More Shubham, Lamsoge Vishal, Puramkar Balwesh, Dandhare Gayatri, and Aditya Turankar Prof. "Cloud Formation (IaC): Deploying a Containerized Application on Cloud." Recent Trends in Cloud Computing and Web Engineering 5, no. 2 (2023): 39–49. https://doi.org/10.5281/zenodo.7936767.
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<em>Infrastructure as Code (IaC) is a practice that automates the deployment and management of infrastructure resources using machine-readable files, which describe the desired state of the desired state of the infrastructure. In this way, the infrastructure is treated as a code and is versioned, tested, and deployed like any other software artifact. Cloud providers offer IaC tools that facilitate the deployment of resources in a scalable and reproducible manner.</em> <em>Containers have become the preferred way to package and deploy applications due to their portability, isolation, and scalability. Container orchestration platforms such as Docker simplify the management of containerized applications by automating the deployment, scaling, and monitoring of containerized workloads.</em> <em>In this context, deploying a containerized application on the cloud involves defining the infrastructure resources required to support the application, such as virtual machines, load balancers, and storage volumes, using IaC tools. Once the infrastructure is defined, the containerized application is deployed on a container orchestration platform such as Docker, which manages the containers and their dependencies. This process enables the deployment of applications in a scalable, fault-tolerant, and cost-effective manner, while reducing the time and effort required to manage the underlying infrastructure.</em> <em>In summary, the use of IaC and container orchestration platforms has revolutionized the way applications are deployed and managed on the cloud. These practices enable developers to focus on writing code rather than managing infrastructure, while ensuring that the infrastructure is deployed in a scalable, reproducible, and cost-effective manner.</em>
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Divya Kodi. "Efficient CI/CD Strategies: Integrating Git with automated testing and deployment." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 1517–30. https://doi.org/10.30574/wjarr.2023.20.2.2363.
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Continuous Integration( CI) and Continuous Deployment (CD) allow prompt delivery of quality software with lesser disruption between development teams. With this simple yet effective Process, Git is a superb tool; the utility combination of Git with some YAML CI/CD templates makes us a better CI/CD pipeline. Keywords: Continuous integration and delivery, Continuous integration and delivery with Git, Automated testing and deployment, Lean continuous integration and delivery, Agile software development 1. Introduction Continuous Integration (CI) and Continuous Delivery (CD) are two of the most vital methodologies in today's software development environment, as they aid developers in developing, testing, and releasing software in much shorter cycles. This study examines an overview of lean CI/CD with Git - automated testing and deployment methods and techniques and gives an extensive overview of approaches and methods providing tools for lean CI/CD sessions. When Git is embedded in CI/CD workflows, code can seamlessly be transferred between development, test, and production environments to give immediate feedback and reduce downtime. These tools simplify the automatic merging of code, running of tests and deployment, thereby reducing human mistakes, increasing productivity, and speeding up a the process of delivering a higher quality software. CI/CD Tools (Jenkins, GitHub Actions, GitLab CI, etc) are there to automate these processes and help out with the scale. CI/CD involves automated testing to make sure you have sufficient quality and reliability in your software. These tests include unit tests, integration tests, and end-to-end tests, which help developers spot and address issues earlier and prevent defects from escaping from one level of system development to another, making this process less resource-intensive. Moreover, deployment automation (e.g., Docker and Kubernetes) allows organizations to deliver continuously without having to depend too much on people. Then, blue-green and canary deployments allow for updates with zero downtime. It further delves into the economic and operational impacts of adopting CI/CD pipelines. Hence, time-to-market is reduced, resources are utilized better, and teams collaborate well, etc. Thus, resorts to containerization and orchestration technologies render deployments cars scalable and reliable, even in complex ecosystems. Configuration drift, manual errors, etc, are some of the bottlenecks for maintaining large-scale environments and Infrastructure as Code ( IaC ) tools ( Terraform, AWS CloudFormation, etc.) allow for consistent and repeatable processes for deployment. Companies who have adopted CI/CD strategies share how it changed their processes via case studies. By integrating Docker-based deployments with GitLab CI/CD, an e-commerce company was able to increase their deployment speed by as much as 40%, while trunk-based development paired with Jenkins allowed a financial services company to increase their test coverage and speed up releases. These are just some of the many very real benefits of incorporating Git, automated testing, and deployment strategies into a CI/CD process. While they are useful, providing CI/CD pipelines can be complicated. The challenges of scaling pipelines to large repositories, securing secrets and toolchain compatibility are non-trivial. In this paper, we will point out these challenges and suggest steps to overcome them. To help organizations get the most out of CI/CD in their workflows, best practices such as enforcing consistent branching policies in CI/CD tools, integrating quality gates (like SonarQube), and using comprehensive monitoring and logging systems are also covered. Looking forward, new trends, including AI-based CI/CD, serverless pipelines and GitOps, will transform the software delivery landscape. The AI-powered predictive analytics will be used to call relevant offerings from the productivity pipeline, and the serverless architectures will reduce the infrastructure load to run the machine-learned tasks. Taking this a step further is GitOps, a newer paradigm centred around declarative infrastructure management, which can help simplify complex deployments.
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-, Anishkumar Sargunakumar. "The Impact of Infrastructure as Code (IaC) on Modern Software Development." International Journal For Multidisciplinary Research 4, no. 1 (2022). https://doi.org/10.36948/ijfmr.2022.v04i01.36900.
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Infrastructure as Code (IaC) has revolutionized modern software development by automating infrastructure provisioning and management. This paper explores the benefits, challenges, and best practices associated with IaC. It highlights key technologies such as Terraform, Ansible, and AWS CloudFormation, providing code examples to illustrate their application. The paper also discusses the impact of IaC on DevOps, security, and business agility. Finally, it concludes with an analysis of future trends in IaC [1][2].
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MacDonald, Graham, Alex Engler, Jeffrey Levy, and Sarah Armstrong. "Spark for Social Science." International Journal of Population Data Science 3, no. 5 (2018). http://dx.doi.org/10.23889/ijpds.v3i5.1044.
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Urban has developed an elastic and powerful approach to the analysis of massive datasets using Amazon Web Services’ Elastic MapReduce (EMR) and the Spark framework for distributed memory and processing. The goal of the project is to deliver powerful and elastic Spark clusters to researchers and data analysts with as little setup time and effort possible, and at low cost. To do that, at the Urban Institute, we use two critical components: (1) an Amazon Web Services (AWS) CloudFormation script to launch AWS Elastic MapReduce (EMR) clusters (2) a bootstrap script that runs on the Master node of the new cluster to install statistical programs and development environments (RStudio and Jupyter Notebooks). The Urban Institute’s Spark for Social Science Github page holds code used to setup the cluster and tutorials for learning how to program in R and Python.
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Dr, Bheemaiah Anil Kumar. "Taskoids: A Formal Definition." August 25, 2019. https://doi.org/10.5281/zenodo.3376813.
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<strong>Abstract:</strong> <strong>A formal definition of taskoids with a future market in trading in work credits with carbon credits and the happiness index clock. Taskoids are defined with a machine genome basis as the quantification of the automation of tasks. They stem from the natural programming, mathematical programming and automated persistence cloud model of computing with computer-assisted code generation. In this paper, we formally define a taskoid and use AWS infrastructure as a service to define IAC rules, encrypted in machine genome to customize solutions using AWS for tasks using a machine genome to transcript YAML or JSON representations.</strong> <strong>Keywords: Taskoids, Quantification, machine genome, cloud computing, YAML, JSON</strong> <strong>What:</strong> <strong>Formal Definition of Taskoids. </strong> <strong>The formal definition of machine genome.</strong> <strong>Definition and classification of task computability as the formulation of automation.</strong> <strong>Example of an AWS CloudWatch and CloudFormation based JSON based Taskoid.</strong> <strong>How:</strong> <strong>Taskoids are meta-programs that use existing codebases and automated coding to encrypt in machine genome a quantIfication of automation.</strong> <strong>Why:</strong> <strong>Similar to Soul Machines and Digital DNA of Digital Human designs, we create taskoids with machine genome to easily or automatically configure IAC to an application for a class of task computability.</strong> <strong>Summary:</strong> <strong>Main Points:</strong> <strong>Two sets of theorems are presented:</strong> <strong>Existence theorems.</strong> <strong>Classification theorems.</strong> <strong>The classification ZT and QT are defined for the expert system of quantum and stochastic algorithms for many taskoids, for which an expert system is described.</strong> <strong>Applications:</strong> <strong>RPA and DPA based automation and work automation using Alexa Business skills for all tasks as proven by the existence theorem for SaaS-based solutions to automation., hence taskoid futures can be written for all possible applications needing work automation. The classes ZT and ST, define new algorithms with an expert system, which is searchable for possible combinations of taskoids for work automation.</strong> <strong>Code Base:</strong> <strong>GitHub Repository</strong> <strong>Website</strong>
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-, Ravi Chandra Thota. "Enhancing Resilience in Cloud-Native Architectures Using Well-Architected Principles." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 6 (2020). https://doi.org/10.37082/ijirmps.v8.i6.232183.
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Organizations depend on enhanced cloud-native architecture resilience because it guarantees operation continuity and reliability within the fast-changing cloud computing environment. This article examines why well-architected principles operate as a structure for creating resilient cloud-native systems. Organizations can reduce architectural failure risks through best practice adherence in their deployment phase, management cycle, and design phase. The author explains through a discussion that scalability with fault tolerance and automatic recovery functions combine to create robust cloud-native systems. Organizations that implement well-architected principles gain resilience capability and become more innovative and agile in their digital business environment of competition. Cloud-native architectures use all capabilities of cloud computing as their primary design principle. New applications that organizations create using these technologies become adaptable and maintain continuous operations despite failure occurrences. The growing adoption of these architectural designs by businesses has made resilience needs more critical. A system demonstrates resilience when it survives breakdowns and backs itself up after such events to maintain operational capability and performance quality. A well-architected framework is a basic framework that enables organizations to meet this objective. The five pillars of operational excellence security, reliability, performance efficiency, and cost optimization allow organizations to build systems that adapt to present needs and anticipated future demands. Fault tolerance stands as a central element for increasing resilience in measurement systems. A system follows this principle through correct operation despite component failures. Magical applications built with microservices architecture face minimized overall impact because a failed service lets other functional services carry on operating. The stability of distributed systems grows better by using service replication within multiple regional domains. The system implements automatic traffic redirection to operational regions if an outage occurs so users maintain access to the service. Organizations that conduct routine failure testing through chaos engineering discover architectural weaknesses to act ahead of potential failure points. Cloud-native architectures become more resilient through the automated recovery principle,essential in enhancing system recovery. Automation streamlines the process and reduces the human work necessary to recover disrupted services. Data restoration happens rapidly through automated backup systems, which decreases system outages and protects against data loss when deployed. Through infrastructure as code (IaC) methodologies, organizations can rapidly re-launch their applications and services,enabling minimally assisted operation in case of failure. Organizations achieve fast environment re-creation after failures when they define their infrastructure through code using tools such as Terraform or AWS CloudFormation. The automation standard enables quicker restoration periods while eliminating human mistakes and strengtheningcloud-native system resistance. Organizations require integrating well-architected principles into cloud-native architectures to strengthen their resistance to adversities.Implementing well-architected principles in digital operations allows businesses to protect themselves from violations and simultaneously develop their ability to generate innovations while adjusting to market changes. A commitment to resilience will enable companies to maintain strong and reliable cloud-native architecture implementation that meets their strategic requirements.