The Impact of Machine Learning (ML) Driven Algorithm Ranking and Visualization on Task Scheduling in Cloud Computing - Chhabra, Manish; Arora, Gagandeep (2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), 2023)
Efficient task organization is essential to maximizing cloud computing's performance. We provide a new machine learning (ML) approach to evaluate and rank task scheduling algorithms based on their features. By employing GDrive and the Spacy English model for feature extraction, we identify and quantify important phrases related to algorithm characteristics. We establish the relative importance of each attribute by giving these terms precedence based on our methodology, particularly based on their frequency. We then combine these priorities to get priority scores for each algorithm, which reveal the algorithms' performance potential. An analysis of the priority ratings can be used to determine the best work scheduling algorithm. The X- Y plot that displays these ratings makes it easier for people to comprehend and contrast the various algorithms. With the help of our innovative method, users-whether personal or business-can make well-informed decisions that maximize cloud resource use and boost overall productivity. In order to optimize cloud computing efficiency, this research paper suggests a machine learning-driven idea backed by feature to follow navigate several work algorithms for scheduling. In cloud environments, artificial intelligence's power greatly aids in achieving greater resource efficiency and enhanced performance.