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Computer Science

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

Profile image of Ramesh Krishna MahimalurRamesh Krishna MahimalurProfile image of International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEITInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

2024, International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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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.

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