Abstract-Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers' requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.
The coronavirus disease 2019 (Covid-19) pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.
The pandemic of Covid-19 has drawn significant attention of the people around the world. The current situation has revealed that this virus infected more than 50 million people globally. On the other hand, the study on crowd simulation can demonstrate the behaviour of massive people that gather in the same location. The importance of this study can lead to safe evacuation in case an outbreak happens. This paper proposed a solution of innovative crowd simulation supported by reinforcement learning and pandemic factors. The study has successfully demonstrated the entering and leaving the various exit and entrance. The experiment is performed with a different mode such as two elevators for exit and entry, staircase and six lanes of gates. As a result of the experimental study, it reveals around 87% of agent behaviour has similarity compared to a real-life simulation performed by previous research. It means the simulation is reflecting the real-life human behaviour when the evacuation process has occurred. The future study can extend to a contagion model of crowd behaviour where an agent can influence each other in a particular situation.
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