AI-Driven Adaptive Network Capacity Planning for Hybrid Cloud Architecture

Abstract

Abstract: Organizations have been increasingly adopting hybrid cloud architectures that integrate private and public cloud deployments to leverage the benefits of both environments. However, this hybrid approach poses a challenge in making accurate predictions on network traffic needs between the private and public cloud constituents due to the dynamic nature of scaling workloads. Traditional capacity planning techniques are inadequate in coping with the quick variances that occur in cloud workloads. This challenge has led to the emergence of AI-based adaptive network capacity planning as a viable option that employs advanced machine learning (ML) and deep learning (DL) technologies to predict future patterns in network traffic with accuracy and dynamically assign network resources within hybrid clouds. This paper proposes an AI model that continuously learns from real-time network traffic data, workload information, and historical trends to predict future network capacity needs and dynamically adjust resources accordingly. The proposed approach involves a hybrid architecture combining Long Short-Term Memory (LSTM) neural networks for capturing temporal patterns and ensemble learning techniques for handling non-linear relationships and complex feature interactions. By leveraging the strengths of both paradigms, the AI model aims to capture complex patterns and dependencies within the data, enabling accurate predictions and proactive resource scaling in hybrid cloud architectures.