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- W4297369690 abstract "In cloud computing environments, parallel processing is required for large.scale computing tasks. Two different tasks are taken, and these tasks are independent of each other. These tasks are independently applied to Virtual Machines (VM). We proposed Offbeat Load Balancing (LB) Machine Learning algorithm using a task scheduling algorithm in Cloud Computing (CC) environments to reduce execution time. In this paper, the proposed algorithm is based on the concept of Random Forest Classifier and Genetic Algorithm and K-Means clustering algorithm for optimized load. The proposed algorithm shows that the average execution time of 3.5104 seconds (20 jobs, 5 Machines) and 15.85 seconds (20 jobs, 10 machines) is based on a study of load balancing algorithms that needs less execution time than other algorithms." @default.
- W4297369690 created "2022-09-28" @default.
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- W4297369690 date "2022-09-22" @default.
- W4297369690 modified "2023-10-16" @default.
- W4297369690 title "Offbeat Load Balancing Machine Learning based Algorithm for Job Scheduling" @default.
- W4297369690 doi "https://doi.org/10.2174/9789815036091122010007" @default.
- W4297369690 hasPublicationYear "2022" @default.
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