AI-Augmented Continuous Integration for Dynamic Resource Allocation
Devops Automation Engineer.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 355–368.
Article DOI: 10.30574/wjaets.2024.13.1.0425
Publication history:
Received on 09 August 2024; revised on 17 September 2024; accepted on 19 September 2024
Abstract:
As the market for software development continues to grow and become increasingly saturated, integrating artificial intelligence into the continuous integration process provides a huge opportunity to optimize cloud resources. This paper discusses the development of AI models that can predict and further adjust the cloud resource in the CI phase based on the historical pipeline performance data and workload trends. With the help of LSTM networks and RL algorithms, the proposed models can optimize resource utilization, decrease costs, and avoid over-provisioning. The mentioned approach implies data gathering and preparation, model training, and Integration with other tools of CI/CD processes. The evaluation shows that resource utilization has been optimized while the number of idle resources has decreased, and resource costs are lower than other resource allocation methods. In addition, the DevOps teams' feedback reveals improved confidence in the resource management decision-making based on the AI-derived data. This work also highlights how incorporating AI into CI can enhance the management of cloud resources to enhance software development productivity. The future research directions are as follows: Model interpretability, standard integration frameworks, and ethical issues in AI-based resource allocation.
Keywords:
Artificial Intelligence (AI); Machine Learning (ML); Continuous Integration (CI); Cloud Resource Allocation; and Dynamic Resource Management
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0