Cost-Aware Multi-Cloud Resource Allocation using Predictive Analytics

Abstract

Multi-cloud environments have become increasingly prevalent due to their ability to enhance reliability, flexibility, and scalability in cloud services. However, managing resource allocation across multiple cloud providers presents significant challenges, especially concerning cost optimization. This paper proposes a cost-aware multi-cloud resource allocation framework that leverages predictive analytics to forecast workload demands and optimize resource distribution dynamically. By analyzing historical usage patterns and real-time data, the proposed approach predicts resource requirements with high accuracy, enabling proactive allocation that minimizes costs while maintaining service level agreements (SLAs). The framework incorporates machine learning models to forecast workload spikes and resource consumption trends, which inform a decision engine to allocate resources efficiently across multiple cloud providers based on pricing, performance, and availability. We evaluate the framework using a realistic multi-cloud simulation environment, comparing it against traditional allocation strategies. Results show significant cost savings, improved resource utilization, and better SLA compliance, demonstrating the effectiveness of predictive analytics in multi-cloud resource management. The paper concludes by discussing the challenges of implementing predictive models in dynamic cloud environments and outlines directions for future research to enhance scalability and adaptability.