Multi-Tiered Security Framework for Container-Based Secure Data Migration in Cloud Environment Using Hardy Weinberg-Apiary Organization-Based Optimization Algorithm
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Abstract
Cloud data migration transfers services, applications, and data from local infrastructure to the cloud to improve scalability, flexibility, and cost efficiency. However, migration faces challenges including security risks, data loss, downtime, performance degradation, and
energy consumption. To address these issues, this study proposes a multi-tiered security framework consisting of five phases: encryption, load balancing, virtual machine (VM) selection, VM privacy protection, and data migration. User tasks are collected and allocated to containers for efficient resource utilization. A novel document-based chunking Blowfish algorithm (DBC-BA) enhances data confidentiality and processing speed during encryption. For load balancing, the proposed LPF-DBSCAN algorithm employs Local Poisson Forest density estimation to improve clustering accuracy under varying data densities. VM selection is optimized using the Hardy Weinberg–Apiary Organization-Based Optimization Algorithm (HW-AOBOA), while T-Closeness protects sensitive VM information. Implemented in Python, the proposed method achieves secure cloud data migration with low energy consumption (38.56 J) and reduced delay (1857 ms).
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