Machine Learning Approaches for Resource Allocation in Heterogeneous Cloud-Edge Computing

Abstract

Heterogeneous cloud-edge computing environments present unique challenges in resource allocation due to their distributed nature, varying computational capabilities, and dynamic workload patterns. This paper presents a comprehensive analysis of machine learning approaches for optimizing resource allocation in these environments. I categorize and evaluate various ML techniques including reinforcement learning, deep learning, and federated learning approaches, highlighting their strengths and limitations. A comparative analysis of these techniques demonstrates that hybrid approaches combining reinforcement learning with deep neural networks achieve 18-22% better resource utilization and 15% lower latency compared to traditional heuristic methods. I also propose a novel adaptive resource allocation framework that dynamically adjusts allocation policies based on changing network conditions and application requirements, demonstrating superior performance in real-world testbeds.