Integrating 5G and Machine Learning for Optimized Resource Management in Cloud Computing

Abstract

Emerging paradigms in cloud computing operations are increasingly recognized as foundational for integrating 5G components and protocols. This integration is critical for enhancing the performance and efficiency of cloud computing data centers, particularly in the context of resource management, as it allows for intelligent adaptations to dynamic network conditions while minimizing manual intervention in operations (Carrozzo et al., 2020). This approach capitalizes on the capabilities of machine learning to drive cognitive processes, enabling networks to self-adapt and efficiently utilize resources in real-time, thereby addressing the complexities inherent in modern cloud environments and ensuring optimal performance across all operational metrics (Carrozzo et al., 2020).

as the demands of emerging applications evolve and require a more flexible architecture that traditional optimization techniques cannot adequately support (Nouruzi et al., 2022). As the demands of emerging applications evolve and require a more flexible architecture that traditional optimization techniques cannot adequately support, the implementation of machine learning algorithms promises to streamline resource allocation processes, enhance predictive maintenance, and ultimately facilitate a more sustainable operational model for cloud computing environments (Morariu et al., 2020) (Nouruzi et al., 2022) (Shehzad et al., 2022).