Automated Cloud Service Instance Testing for Warm Bootup Using Multi-Agent Systems

Dharmeesh Kondaveeti () and Mohan Vamsi Musunuru ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 7, issue 01, 327-338

Abstract: Ensuring rapid and reliable warm bootup of cloud service instances is critical for maintaining high availability, minimizing downtime, and delivering consistent user experiences in dynamic cloud environments. This paper presents an automated testing framework that leverages multi-agent systems to evaluate, monitor, and optimize warm bootup performance across heterogeneous cloud service architectures. The proposed approach integrates autonomous agents with specialized roles—such as resource monitoring, dependency validation, and performance benchmarking—to conduct parallelized test executions and diagnose bottlenecks in real time. Experimental results from a hybrid cloud deployment demonstrate that the multi-agent strategy reduces test execution time by up to 38% while improving fault detection accuracy by 27% compared to conventional sequential testing methods. This work highlights the potential of intelligent agent coordination to enhance scalability, adaptability, and operational efficiency in automated cloud instance testing scenarios.

Keywords: Automated testing; cloud services; warm bootup; multi-agent systems; performance optimization; fault detection; hybrid cloud; service reliability; autonomous agents (search for similar items in EconPapers)
Date: 2024
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