MODIFIED SOFTWARE DEPLOYMENT ALGORITHM USING MULTI-THREADING

The article presents a modified deployment algorithm for software systems using multithreading in AWS CodeBuild, aimed at optimizing build time and reducing computational resource costs in cloud environments. The key stages of the build process, including parallel test execution, task allocation analysis, and resource management, were modeled using finite automata, timed automata, and Petri nets. Particular attention was given to identifying and addressing the limitations of AWS CodeBuild's standard parallelization mechanisms, which can lead to inefficient resource utilization and extended build durations. The study revealed that AWS CodeBuild's default mechanisms are not always capable of optimally leveraging system resources, especially when handling large software projects with numerous dependencies. To overcome these limitations, the use of Python's multithreading capabilities was proposed as a convenient tool for extending the platform's base functionality. The proposed approach enabled flexible thread management and task distribution at the user scenario level, significantly reducing overall build time. Experimental results demonstrated substantial reductions in build execution time compared to the default AWS CodeBuild settings, confirming the effectiveness of the proposed algorithm in enhancing performance and ensuring high scalability for build processes in cloud environments. The developed algorithm is particularly relevant for large software projects requiring frequent iterative builds and testing. The findings can be utilized to improve automated deployment processes and computational resource management in cloud ecosystems.