Comparative Analysis of Job Scheduling Algorithms Enhanced by Machine Learning (KEY IJP************888)

• Manthan Mohanrao Thakre,Aniket Gowardhan Banarase,Prof. P. B. Jaipurkar

Abstract

Survey explores the integration of machine learning techniques into job scheduling algorithms, aiming to enhance efficiency and adaptability in dynamic computing environments. We systematically compare various job scheduling methods, highlighting traditional approaches alongside innovative machine learning models. Our review covers key performance metrics, such as execution time, resource utilization, and scalability, while examining the strengths and weaknesses of each algorithm. Additionally, we discuss the role of predictive analytics and adaptive learning in optimizing scheduling decisions. This comprehensive analysis serves as a foundation for future research, guiding the development of more intelligent and responsive job scheduling systems in cloud computing, data centers, and distributed environments.