Adaptive AI-Driven Multi-Cloud Scheduler for Improving Load Balancing and Performance Optimization in Cloud Computing

Abstract

Cloud computing is one of the most efficient digital infrastructures providing scalability and cost efficiency. Nonetheless, the expansion in multi cloud environments has caused a swift increase in the complexity of challenges like resource optimization, load balancing, and response time efficiency. In this paper, we discuss the implementation of an adaptive AI based multi-cloud scheduler (AIMS) to help resolve these dire issues. The scheduler works through the use of machine learning algorithms and other adaptive real time techniques to ensure that resources are used properly and that performance across systems is enhanced. Results demonstrate that AIMS outperforms existing scheduling mechanisms in respect to latency, throughput, and load balancing. This paper is a step in the dividing directions of multi-cloud heterogeneous infrastructures with self adaptive cloud resource management.