Jaya dung beetle optimization-based load balancing and VM Migration for cloud data security in DevOps

In cloud computing applications different development strategies like (Development and Operational) DevOps are deployed due to their wide applications. At the same time, migration from the development framework to an organization framework is required for deploying various applications in the cloud...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 124; p. 110400
Main Authors: Reddy, Yanamala Harinatha, Rao, D B Jagannadha, Polepally, Vijayakumar
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2025
Subjects:
Adaptive variable length VM encryption
Dung beetle optimization
Jaya optimization
Source code change detection
Source code change detection
Jaya optimization
Adaptive variable length VM encryption
Dung beetle optimization
ISSN:0045-7906
Online Access:Get full text
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Summary:In cloud computing applications different development strategies like (Development and Operational) DevOps are deployed due to their wide applications. At the same time, migration from the development framework to an organization framework is required for deploying various applications in the cloud to ensure scalability and optimal resource usage in DevOps. Various security vulnerabilities occur in the deployment environment during the migration process. The inherent complexity of DevOps along with the absence of proper encryption schemes make it vulnerable to attacks. To address this issue, the Jaya Dung Beetle Optimization (JDBO) approach is designed for balancing load and performing Virtual Machine (VM) migration. Here, the code is configured initially using the DevOps code repository, and the code processor is exploited for handling the version control. The source code changes are determined and data is encrypted. Later, VM is categorized as underloaded and overloaded by utilizing Deep Embedded Clustering (DEC) and the load is computed. VM migration and load balancing are effectuated using JDBO. Finally, the deployment of the VM is carried out again and the VM data is decrypted. Moreover, the JDBO observed resource utilization, load, mitigation costs, time complexity, and capacity of 0.934, 0.065, 8.5, 0.439 s, and 90.79 MB.
ISSN:0045-7906
DOI:10.1016/j.compeleceng.2025.110400