AI-Powered Graph Analytics for Optimizing Distributed Computing in MultiCloud Environments

Abstract

The proliferation of multi-cloud environments has created a need for advanced techniques to optimize distributed computing processes and manage massive datasets efficiently. This paper explores the integration of artificial intelligence (AI) and graph analytics to address these challenges, leveraging their combined capabilities to enhance resource allocation, minimize latency, and maximize throughput in multi-cloud ecosystems. The proposed framework utilizes graph-based representations of cloud resources, tasks, and network topologies to facilitate dynamic decision-making and real-time optimization. AI-driven algorithms, including reinforcement learning and graph neural networks (GNNs), are employed to analyze complex interdependencies and predict optimal resource configurations. Experimental results demonstrate significant improvements in computational efficiency, reduced operational costs, and enhanced system reliability. This research underscores the transformative potential of AI-powered graph analytics in redefining distributed computing paradigms for multi-cloud environments.