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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. The objectives of this work are: i) to provide a comprehensive review of the cloud and scheduling process; ii) to classify the scheduling strategies and scientific workflows; iii) to implement our proposed algorithm with various scheduling algorithms (i.e., Min-Min, Round-Robin, Max-Min, and Modified Max-Min) for performance comparison, within different cloudlet sizes (i.e., small, medium, large, and heavy) in three scientific workflows (i.e., Montage, Epigenomics, and SIPHT); and iv) to investigate the performance of the implemented algorithms by using CloudSim. …”
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Batch mode heuristic approaches for efficient task scheduling in grid computing system
Published 2016“…Many algorithms have been implemented to solve the grid scheduling problem. …”
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SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment
Published 2024“…To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language.…”
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Contextualising Computational Thinking: A Case Study in Remote Rural Sarawak Borneo
Published 2020“…The paper describes an exploratory case study on novice indigenous children’s learning characteristics as they learn Computational thinking (CT) competencies, such as abstraction, decomposition, and algorithmic thinking. …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
Published 2022“…The tri-agent reinforcement learning (TA-RL) algorithm has been used by the segment spectrum agent (SSA) in this suggested CSS to make the global decision. …”
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Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection
Published 1998“…The neural networks c1assifier was very successful in classifying 1 3 different image patterns by learning from 4 training patterns. The classifier also clocked an average speed of 9.6ms which makes it feasible to implement it on the production floor. …”
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Machine Learning Based Two Phase Detection and Mitigation Authentication Scheme for Denial-of-Service Attacks in Software Defined Networks
Published 2024“…This scheme incorporates machine learning techniques by utilizing Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classification algorithms to accurately identify and handle malicious network traffic following the initial packet filtration process that identifies abnormal traffic. …”
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