Publication

Abstract :

Cloud computing is a paradigm shift in the way computing resources are delivered and consumed.The demand for cloud computing services has led to an increase in the complexity of cloud systems, making load balancing an essential component of their performance and reliability. In this paper, we propose an enhanced approach to cloud computing load balancing that not only improves the performance and reliability of cloud systems, but also finds correlations between resources and applications. Algorithms in Artificial Intelligence and Machine Learning aid in the dynamic and real-time resource allocation, taking into account multiple factors such as traffic patterns, resource utilization, and service level agreements. Our approach goes beyond traditional load balancing methods by also discovering correlations between resources and applications, providing valuable insights into the relationships between different components of the system. The interpretation proves to be helpful in optimizing resource allocation which in turn corresponds to the prevention of performance degradation. Our evaluation results in the enhanced performance with respect to latency and resource utilization, as well as a clear understanding of the correlations between resources and applications. This work highlights the potential for AI and ML to not only enhance cloud computing load balancing, but also provide valuable insights for optimizing resource allocation and improving the overall performance of cloud systems.