Game Theory Approaches to Sustainable Resource Allocation in Cloud and AI Systems: A Review

Abstract

Algorithmic game theory is a popular concept that has numerous applications like designing mathematical frameworks for analyzing interactions between rational agents which has become increasingly important in artificial intelligence and cloud computing for efficient resource allocation and optimization that helps users in saving money and resources. This study examines the applications of classical and modern game theory concepts to these kinds of energy efficient workloads and cloud infrastructure management in general using various algorithms. The main objective of this study is to review key concepts like normal form games, Nash equilibrium, mixed strategies and mechanism design to evaluate their scope in scalable and systems that are based on incentives with an analysis of auction mechanisms like Vickrey Clarke Groves model and congestion games which helps us formulate a unified modeling framework using utility functions, congestion analysis and mechanism design. The results also provide a look into tradeoffs between system efficiency, scalability and energy consumption in these systems where the findings indicate that game theory mechanisms improve allocation efficiency, fairness and energy utilization in cloud systems that are shared and helps in reducing substantial computational overhead. However, challenges related to scalability, dynamic pricing and live decision making are still prevalent. Overall, algorithmic game theory offers a strong foundation for developing sustainable, intelligent and energy efficient artificial intelligence in cloud computing systems and is expected to support future green computing architectures.