Hybrid Task Scheduling Using Genetic Algorithms and Machine Learning for Improved Cloud Efficiency

Shivaraj Yanamandram Kuppuraju; Prasanna Sankaran; Sambhav Patil

doi:10.36948/ijfmr.2025.v07i02.39380

Hybrid Task Scheduling Using Genetic Algorithms and Machine Learning for Improved Cloud Efficiency

Author(s)	Mr. Shivaraj Yanamandram Kuppuraju, Prasanna Sankaran, Sambhav Patil
Country	India
Abstract	This research presents a hybrid task scheduling approach that integrates genetic algorithms and machine learning to enhance cloud computing efficiency by optimizing resource utilization, reducing makespan, minimizing energy consumption, and improving overall system throughput. Traditional scheduling techniques such as First-Come-First-Serve, Round-Robin, and heuristic-based methods often suffer from inefficiencies due to static decision-making and lack of adaptability to dynamic workloads. The proposed model leverages genetic algorithms for evolutionary optimization while incorporating machine learning-based predictions to enhance task allocation and resource management. Reinforcement learning further refines scheduling policies by continuously learning from past scheduling decisions and adjusting strategies in real-time. Experimental evaluations conducted using CloudSim demonstrate that the Hybrid GA + ML approach significantly outperforms conventional scheduling methods across key performance metrics, achieving a 40% reduction in makespan, a 32% improvement in energy efficiency, and a 25% increase in throughput. The intelligent load-balancing mechanism ensures optimal resource distribution, preventing bottlenecks and enhancing system stability. Despite the computational overhead introduced by machine learning, the efficiency gains outweigh the costs, making this approach viable for real-world cloud environments. The study highlights the potential of integrating evolutionary algorithms with predictive analytics to create more adaptive and efficient scheduling frameworks for modern cloud infrastructures. Future work can further enhance scalability and security by incorporating federated learning, decentralized optimization techniques, and privacy-preserving machine learning mechanisms to ensure robust and intelligent cloud task scheduling.
Keywords	Cloud task scheduling, genetic algorithms, machine learning, resource optimization, energy efficiency
Field	Computer
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-18
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.39380
Short DOI	https://doi.org/g89vt8

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Hybrid Task Scheduling Using Genetic Algorithms and Machine Learning for Improved Cloud Efficiency

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