AI-Assisted Cloud Migration

Infrastructure as Code, also named Infrastructure-Aware Software, is one of the roots of modern software deployment tactics in clouds noted by providing high efficiency, measurability and modularity. This paper focuses on AWS Cloud Formation, the transformative and efficient IaC tool that simplifies, scales, and secures software instantiation. As more organizations adopt cloud environments, they experience challenges that arise from cloud infrastructure management. AWS Cloud Formation helps overcome these challenges by presenting a single, declarative, resource-provisioning model. In this paper, you learn the value of AWS Cloud Formation, such as its capacity to create and manage resources, including EC2 instances, VPCs, and load balancers for you. The paper also discusses further recommendations for considerations of AWS Cloud Formation templates in scaling and deploying applications, meeting the requirements of multi-Region applications, as well as the utilization of IAM policies and metadata for encryption into the application. Additionally, we explain use cases of Cloud Formation in different sectors and its use in conjunction with DevOps tools and CI/CD pipelines and the automation tools AWS Code Pipeline and AWS Code Build. In addition to this, we provide a literature review of the development of IaC and a comparison of IaC from different cloud computing platforms, as well as a comparison of how Cloud Formation differs from Terraform and other related tools. Furthermore, we provide best practices in general for Azure ARM and especially refine an already existing guide for AWS cloud formation for an enterprise environment, complemented with real examples. In the results and discussion section, we provide the extent to which Cloud Formation has helped minimize human error, made operations more efficient, and increased security in the cloud environment. Finally, we present a conclusion containing recommendations and possible prospects for the development of Cloud Formation in the context of a rapidly changing base of cloud services. Keywords: Infrastructure as Code (IaC), AWS Cloud Formation, DevOps, Multi-region Deployments, Resource Provisioning.

Organizations are now seriously considering adopting cloud into the existing business context, but migrating data, application and services into cloud doesn't come without substantial risks. These risks are the significant barriers for the wider cloud adoption. There are works that consolidate the existing work on cloud migration and technology. However, there is no secondary study that consolidates the state of the art research and existing practice on risk management in cloud computing. It makes difficult to understand the risks management trend, maturity, and research gaps. This paper investigates the state of the art research and practices relating to risk management in cloud computing and discusses survey results on migration goals and risks. The survey participants are practitioners from both public and private organizations of two different locations, i.e., UK and Malaysia. The authors identify and classify the relevant literature and systematically compare the existing works and survey results. The results show that most of the existing works do not consider the existing organization and business context for the risk assessment. The authors' study results also reveal that risk management in cloud computing research and practice is still not in a mature stage but gradually advancing. Finally, they propose a risk assessment approach and determine the relative importance of the migration goals from two real migration use cases.

Cloud migration projects are increasingly vital for modernizing legacy applications and enhancing business agility. This paper examines the comprehensive process of migrating an ATG-based e-commerce application to Google Cloud Platform (GCP). The migration strategy encompassed thorough planning, analysis, and implementation stages that addressed key aspects of scalability, performance optimization, and security enhancement. Initially, a detailed assessment of the existing ATG framework identified potential bottlenecks and compatibility issues with cloud environments. Subsequently, a robust migration roadmap was developed to mitigate risks and ensure a seamless transition, emphasizing data integrity and minimal service disruption. The process involved refactoring monolithic components into microservices, integrating containerization technologies, and leveraging GCP’s advanced orchestration, monitoring, and automation tools. Throughout the migration journey, several challenges emerged, including legacy code dependencies, integration complexities, and the necessity to comply with stringent industry standards. Addressing these obstacles required iterative testing, close stakeholder collaboration, and agile project management practices. Ultimately, the outcomes demonstrated improved operational efficiency, enhanced system resilience, and a significant reduction in infrastructure costs. Furthermore, the transition enabled the e-commerce platform to capitalize on cloud-native features such as dynamic scaling

As enterprises embrace multi-cloud strategies to enhance agility, reduce vendor lock-in, and meet regulatory requirements, the need for scalable and reliable infrastructure management becomes critical. GitOps a paradigm that leverages Git as the single source of truth for declarative infrastructure offers a transformative approach to manage Infrastructure as Code (IaC) across heterogeneous cloud environments. This paper explores scalable GitOps models tailored for multi-cloud deployments, highlighting key architectural patterns, toolchains, and workflows that enable secure, auditable, and automated infrastructure operations. The study analyzes the strengths and limitations of leading GitOps tools such as ArgoCD and Flux in coordinating cross-cloud configurations and reconcile loops. The study also examines strategies for repository structuring, modularization of IaC, policy-as-code integration, and dynamic secrets management to support enterprise-scale deployments. The study propose a reference architecture that addresses the challenges of scalability, compliance, and resilience in multi-cloud GitOps workflows. My findings demonstrate that, when correctly implemented, GitOps can serve as a powerful operational model for achieving continuous delivery and governance in complex cloud-native ecosystems.

Cloud computing paradigm motivates many companies to adopt and use cloud services to take advantage of the inherent characteristics. Increasing number of companies are adopting cloud computing, but such adoption has not been sufficiently structured to ensure a successful migration to the cloud environment. Until now there has been no framework or paradigm that supports cloud migration to be used conventionally, so the adoption of cloud computing is still done on an ad-hoc. Therefore, in this research we designed an enterprise architecture framework for non-cloud to cloud migration using TOGAF, CCRM, and CRMM that effective and efficient framework to be used as a guide or reference for cloud migration that supports the alignment between business and IT and while considering existing conditions IT at the enterprise. We map phase and steps in TOGAF ADM with CCRM which modification steps, an additional steps, and also decrease steps based on the requirements needed in cloud computing, then adopted CRMM used to categorize cloud migration process to make it more clear. Then, the framework will be vaidated with implementation and user judgement. This research resulted an enterprise architecture framework for non-cloud to cloud migration using TOGAF, CCRM, and CRMM which consists of 4 main processes that are equipped with 10 phase and 93 steps to where in each step contains input, process, output, link processes, and tools, methods, and techniques. From the validation results showed that the cloud migration process on a framework is a good guide that can be used effectively and efficiently to help cloud migration is commonly used by all types of companies or agencies.

Cloud computing technology is getting more advanced these days. Cloud computing is a pay-as-you-go service since it allows for variable resource allocation for services. Task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing among cloud resources. For scheduling resources in cloud environment, different tasks need to be executed simultaneously by the available resources in order to minimize the completion time. To achieve this, we proposed a new technique called Enhanced Max-Min Algorithm (EMM). This algorithm takes less time to complete all the tasks with minimum completion time. In the proposed algorithm, small size tasks are concurrently executed and resource allocation is done accordingly. When compared to Min-Min algorithm the Enhanced Max-Min algorithm provides high resource utilization and reduces the makespan.

Cloud technologies quickly evolve and require efficient ways to process and deploy data for high operational performance and low cost. This study looks at code-driven automation applied to cloud infrastructure, with a focus on Terraform, Jenkins, and Kubernetes. Automating infrastructure provisioning and application deployments, these tools minimize human intervention and potential human error, thus optimizing operational effectiveness. The findings indicate that code-driven automation not only accelerates deployments but also provides scalability and dependability among many cloud environments, thus ensuring fast and efficient delivery of cloud services. With this strategy, organizations can optimize their cloud operations and resource utilization needs. The research helps to understand how effective automation of processes would improve cloud infrastructure management and service delivery in very dynamic technological times. Keywords—Cloud Infrastructure Management, Code-Driven Automation, Infrastructure as Code (IaC), Automated Deployment, Cloud Efficiency, Resource Utilization, CI/CD Pipelines, Kubernetes.

Modern Software-as-a-Service (SaaS) platforms require a strongand reliable infrastructure that can function seamlessly across various cloud providers, without losing consistency and efficiency in operations. The multi-cloud architecture, however, brings in a lot of complexity due to the differences in provider APIs, resource models, and operational characteristics. When infrastructure management is handled manually, configuration drift becomes a significant issue. The conventional deployment methods are not efficient enough to work at a large scale across different cloud environments. The piece lays out an intricate automation framework that combines Infrastructure as Code (IaC) provisioning, multi-level validation testing, and centralized observability in a flawless manner. Using Terraform, one can carry out declarative infrastructure specifications that can be spread over Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Puppet ensures that the standard configurations are kept in the various node populations. Unit testing is employed to confirm the correctness of the individual modules before their integration. Acceptance testing simulates the end-to-end workflows in temporary environments that are exact replicas of production topologies. Deployment of catalog comparison strategies uncovers unintentional configuration changes prior to implementation. Centralized logging collects the events from distributed systems that are open for cross-cutting analysis. Monitoring dashboards draw the time-series metrics from the infrastructure components and the applications. Automated alerting pinpoints the operational issues facing the threshold conditions and anomaly detection. The combination of the framework fills in the critical gaps of the existing single-cloud automation approaches. Declarative specifications are there to remove the complexities of provider-specific. Automated validation is there to stop the propagation of faulty configurations across distributed systems. Unified observability is the consistency of visibility across fragmented multi-cloud landscapes. The outline exhibits the solutions for the realization of enterprise-scale cloud operations by the integration of strategic automation.

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

Migration of legacy application to Cloud is a fast-growing area of knowledge. Many IT-based organizations inclined toward empowering their legacy application with cloud computing capabilities. Many researchers, academicians, national, and international bodies are creating knowledge models to allow knowledge sharing and provide effective cloud migration model. This knowledge is scattered and huge, but lack of knowledge management. Our motive is to produce a metamodel, which could be able to generalize the cloud migration domain. Metamodel approach is an approach, to gather all domain concepts and their relationships. Using the metamodel, variety of domain solution models can be built. It can act as a language infrastructure which unifies describing the process model of moving legacy enterprise applications to the cloud environments. The benefits of the metamodel include simplifying the migration process, guidance, reuse specialized migration knowledge and support training and knowledge management activities. Furthermore, it reduces complexity and ambiguity in cloud migration domain.

The aim of this paper is to allocate resources to tasks and scheduling tasks on existing virtual machines (VMs) in cloud environments, so that the time to finish the last work and average of all tasks execution time are minimized, and loads are distributed balanced on virtual machines. Since task scheduling in the cloud environment is a continuous process, so scheduling improvements, although slight, play an important role in cloud efficiency. On the other hand the resource allocation problem in cloud computing and user tasks scheduling on existing virtual machines is a NP-hard problem, and traditional algorithms requires exponential time to examine search space of this problem in sequence and finding the best answer, therefore we used Gravitational Search Algorithm (GSA) that has a high efficiency in solving nonlinear problems, for solving this problem. To do this, we create masses by combining sequences of tasks assigned to all machines. Each mass position is a solution of the problem. Then we find the best possible assignment using the gravitational search algorithm. We used fuzzy logic to determine the number of masses that affect one another during the implementation of the GSA. To calculate the cost, we use a combination of Make_ span (Time to finish the last task) and Mean_ Flow_Time (Average of all tasks execution time) and Load_ imbalance. The results show that the proposed method achieves more optimal response than genetic algorithm and GSA without fuzzy for resource allocation. It means that proposed algorithm allocated resources to tasks with less make span and mean_ flow time and more load balancing than other two algorithms.

Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may face high costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modelled as a game between clients. A game theoretic modelling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centres. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource.

Migration to the cloud is a significant move for enterprises seeking to transform their IT platforms and remain relevant in a digital marketplace. Existing applications that are based on legacy frameworks are difficult to scale, adjust and maintain affordably. By moving these applications to the cloud, companies can benefit from modern cloud capabilities like on-demand scaling, faster performance and improved security. This paper presents best practices for migrating legacy applications to the cloud, covering various cloud migration techniques such as rehosting, replatforming, and refactoring. It also elaborates on the key elements involved in creating a successful cloud migration roadmap, from assessing the current state of deployment to selecting applications and ensuring secure deployment. Further, the paper goes into design considerations like performance optimization, resilience, and cost to ensure migrated applications are effectively running in the cloud. When implementing a systematic cloud migration while considering both the technical and business implications, businesses will not need to worry about the transition and can fully reap the benefits of cloud computing.

With the advancement of information technology need to perform computing tasks everywhere and all the time there, in cloud computing environments and heterogeneous users have access to different sources with different characteristics that these resources are geographically in different areas. Due to this, the allocation of resources in cloud computing comes to the main issue is considered a major challenge to achieve high performance. Due to the nature of cloud computing is a distributed system to account, comes to business, economic methods such as auctions are used to allocate resources for decentralization. As an important economic bilateral hybrid auction model is the perfect solution for the allocation of resources in cloud computing, on the other hand, providers of cloud resources similarly, their sources of supply combined addressing. One of the problems auction two-way combination with maximum benefit for the parties to the transaction is the efficient allocation of resources to the problem of determining an auction winner is known. Given that the winning auction is NP-Hard. It results in a problem, several methods have been proposed to solve it. In this dissertation, taking into account the strength simulated annealing algorithm, a modified version of it is proposed for solving the winner determination in combinatorial double auction problem in cloud computing. The proposed approach is simulated along with genetic and simulated annealing algorithms and the results show that the proposed approach finds better solutions than the two mentioned algorithms.

Cloud computing is not just about resource sharing, cost savings and optimisation of business performance; it also involves fundamental concerns on how businesses need to respond on the risks and challenges upon migration. Managing risks is critical for a sustainable cloud adoption. It includes several dimensions such as cost, practising the concept of green IT, data quality, continuity of services to users and clients, guarantee tangible benefits. This paper presents a risk management approach for a sustainable cloud migration. We consider four dimensions of sustainability, i.e., economic, environmental, social and technology to determine the viability of cloud for the business context. The risks are systematically identified and analysed based on the existing in house controls and the cloud service provider offerings. We use Dempster Shafer (D-S) theory to measure the adequacy of controls and apply semi-quantitative approach to perform risk analysis based on the theory of belief. The risk exposure for each sustainability dimension allows us to determine the viability of cloud migration. A practical migration use case is considered to determine the applicability of our work. The results identify the risk exposure and recommended control for the risk mitigation. We conclude that risks depend on specific migration case and both Cloud Service Provider (CSP) and users are responsible for the risk mitigation. Inherent risks can evolve due to the cloud migration.