ABSTRACT
In contemporary cloud computing environments, the efficient allocation and utilization of resources are vital to ensure prompt performance and maximize the utilization of the existing infrastructure. The proliferation of cloud platforms has led to the emergence of considerable challenges related to load balancing and efficient task scheduling, since a rising number of applications and services rely on these platforms. This article introduces an innovative approach to tackle these challenges through the utilization of machine learning (ML) techniques. In this study, we propose a comprehensive framework that effectively allocates resources in real-time systems by adapting to their evolving demands. This framework achieves its objectives by integrating algorithms for load balancing and scheduling. Machine learning models, which have been trained using previous data on workloads and system performance, can be utilized to forecast upcoming load surges and identify potential bottlenecks. Subsequently, the computer system proactively modifies the allocation of resources and the arrangement of tasks to preemptively address future challenges. Upon comparison with conventional approaches, the initial findings indicate significant advantages in terms of system performance, decreased latency, and improved resource utilization. Furthermore, the framework’s flexible architecture ensures the capacity to scale and adapt, rendering it well-suited for deployment in dynamic environments such as cloud-based systems that undergo frequent modifications. This study showcases the transformative potential of ML in redefining resource allocation and task scheduling inside cloud computing ecosystems.
