HIERARCHICAL DECISION MODELLING OF CLOUD ENTERPRISE DATA STORAGE SYSTEMS USING AHP
DOI:
https://doi.org/10.37943/WGVS4419Keywords:
Cloud service provider selection, multi-criteria decision making (MCDM), Analytic Hierarchy Process (AHP), cloud computing, quality of service, decision analysis, reliability, performance, strategic alignment, continuityAbstract
Evaluating cloud enterprise data storage systems is mission-critical for ensuring reliability, security and compliance, interoperability, scalability, performance, vendor support, and cost-effectiveness in modern organizations where petabytes of data are stored. However, choosing the appropriate storage solution is complex, as enterprises must consider numerous strategic, technological, and operational dimensions. This article addresses these challenges by presenting a multiperspective assessment of cloud enterprise storage systems supported by multi-criteria decision making (MCDM) tool, such as Analytic Hierarchy Process (AHP). A three-tier hierarchy was constructed that organizes nine criteria into strategic, technological, and operational dimensions. Using this structure, three leading cloud storage platforms – Amazon S3, Microsoft Azure, and Google Cloud Storage – were evaluated. The opinions of twelve senior experts from National Information Technologies Joint-Stock Company (NITEC JSC) in Kazakhstan, all with experience in corporate cloud infrastructure, were collected using pairwise comparison questionnaires and summarized using the geometric mean. Local and global weights were obtained by calculating the priority based on eigenvectors with a consistency coefficient check and sensitivity analysis to confirm the reliability of the model. The results show that the criteria related to continuity and reliability prevail in decision-making, while pure productivity and manageability have a comparatively lower weights. Amazon S3 had the highest overall priority, followed by Microsoft Azure and Google Cloud Storage. This study contributes not only a practical solution for a multiperspective decision model but also a validated methodological blueprint that helps enterprises adapt continuously as technologies evolve. Future work will explore how hierarchical decision modeling can facilitate transparent and repeatable selection of enterprise cloud storage providers and can be expanded to include additional criteria such as cost and security in organization-specific applications.
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