Optimizing Resource Allocation in Multi-Cloud Environments with Artificial Intelligence: Balancing Cost, Performance, and Security

Abstract

The multi-cloud environment has become a strategic choice for the organization to leverage different strengths of cloud service providers. However, orchestrating resources across multiple clouds brings complexity in optimizing cost efficiency, performance, and security compliance. Therefore, this paper presents a conceptual framework that uses artificial intelligence to optimize resource allocation in multi-cloud environments. The proposed model integrates machine learning algorithms with intelligent optimization techniques to predict workload demands and allocate resources dynamically across various cloud platforms. The framework introduces a balanced approach that simultaneously considers the trade-offs between cost, performance, and security. Using predictive analytics, the system forecasts workload patterns and accordingly adjusts resource provisioning in real time. Security considerations are smoothly factored into the optimization process; threat assessment models and compliance checks are incorporated in order to ensure resource allocation decisions that are guaranteed to conform to organizational policies and regulatory requirements. Driven by AI, it is a scalable solution able to adapt itself to the particular needs of various organizations and their dynamic nature of workloads, considering heterogeneity across cloud services. The framework addresses the multi-dimensional challenges in resource allocation, providing a holistic solution to enhance resource utilization, optimize costs, maintain high-performance levels, and guarantee strong security standards. This conceptual model justifies the need for future implementations and research in the refinement of machine learning techniques and the extension of the framework in supporting emerging cloud technologies and services.