AI-Powered Network Optimization for Cloud Service Providers' Compute In-stances During Cold Bootups

Mohan Vamsi Musunuru () and Srikanth Gorle ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2025, vol. 8, issue 1, 264-276

Abstract: Cold bootup events in cloud computing environments often lead to inefficiencies in resource allocation, increased latency, and reduced service responsiveness. This paper proposes an AI-powered network optimization framework specifically designed for Cloud Service Providers (CSPs) to enhance compute instance performance during cold bootups. Leveraging machine learning algorithms, the system dynamically predicts workload demands, preemptively allocates network bandwidth, and optimizes data routing to minimize initialization delays. Experimental results from simulated cloud environments demonstrate up to a 42% reduction in bootup latency and a 27% improvement in throughput compared to conventional methods. The proposed approach not only accelerates service readiness but also improves overall user experience and reduces operational costs for CSPs. These findings highlight the potential of AI-driven strategies in addressing performance bottlenecks in cloud infrastructure provisioning.

Keywords: AI-powered network optimization; cold bootup latency; cloud service providers; compute instance performance; workload prediction; bandwidth allocation; data routing optimization; machine learning in cloud computing; service readiness (search for similar items in EconPapers)
Date: 2025
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