CLOUD MIGRATIONMODELER: A COMPREHENSIVE STUDY ON CLOUD MIGRATION AND ROI EVALUATION

Cloud computing has revolutionized the landscape of modern IT infrastructure, offering unprecedented access to computing resources and scalability.Despite the increasing prevalence of cloud migration initiatives, a significant research gap exists in the development of comprehensive frameworks that address the multifaceted challenges of cloud migration. Existing models often overlook crucial factors such as return on investment (ROI) evaluation, risk assessment, and alignment with business objectives.This paper introduces Cloud MigrationModeler, a model-based methodology addressing this gap by facilitating the transition of enterprise systems to cloud environments.Comprising seven distinct phases, Cloud MigrationModeler encompasses tasks ranging from initial inspection to framework testing and assessment. This paper delves into the financial implications of migration to cloud-based infrastructure, using the Azure App Service P5mv3 plan as a focal point. Through a detailed cost analysis, we aim to assess the potential ROI and strategic benefits associated with this migration. By meticulously calculating the cost differentials and efficiency gains, we offer unparalleled insights into the tangible financial benefits of embracing cloud-based solutions. Together, the introduction of Cloud MigrationModeler and the detailed ROI analysis underscore the pivotal role of research-driven methodologies and data-driven insights in guiding organizations towards successful cloud migration endeavors.This analysis underscores the necessity for a standardized migration framework and strategies.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-19
DOI
https://doi.org/10.5281/zenodo.15394511
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

CLOUD MIGRATIONMODELER: A COMPREHENSIVE STUDY ON CLOUD MIGRATION AND ROI EVALUATION

Zenodo (CERN European Organization for Nuclear Research)
Cloud Computing and Resource Management
article

CLOUD MIGRATIONMODELER: A COMPREHENSIVE STUDY ON CLOUD MIGRATION AND ROI EVALUATION

article en

Abstract

Cloud computing has revolutionized the landscape of modern IT infrastructure, offering unprecedented access to computing resources and scalability.Despite the increasing prevalence of cloud migration initiatives, a significant research gap exists in the development of comprehensive frameworks that address the multifaceted challenges of cloud migration. Existing models often overlook crucial factors such as return on investment (ROI) evaluation, risk assessment, and alignment with business objectives.This paper introduces Cloud MigrationModeler, a model-based methodology addressing this gap by facilitating the transition of enterprise systems to cloud environments.Comprising seven distinct phases, Cloud MigrationModeler encompasses tasks ranging from initial inspection to framework testing and assessment. This paper delves into the financial implications of migration to cloud-based infrastructure, using the Azure App Service P5mv3 plan as a focal point. Through a detailed cost analysis, we aim to assess the potential ROI and strategic benefits associated with this migration. By meticulously calculating the cost differentials and efficiency gains, we offer unparalleled insights into the tangible financial benefits of embracing cloud-based solutions. Together, the introduction of Cloud MigrationModeler and the detailed ROI analysis underscore the pivotal role of research-driven methodologies and data-driven insights in guiding organizations towards successful cloud migration endeavors.This analysis underscores the necessity for a standardized migration framework and strategies.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 38%
Cloud Computing and Resource Management
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CLOUD MIGRATIONMODELER: A COMPREHENSIVE STUDY ON CLOUD MIGRATION AND ROI EVALUATION · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS