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Resource Prediction for Big Data Processing in a Cloud Data Center
Alanazi Rayan,Yunmook Nah 대한전자공학회 2018 IEIE Transactions on Smart Processing & Computing Vol.7 No.6
The high demand for big data applications, such as the Internet of Things (IoT), healthcare, business, and academia, as well as government, fosters the creation of large-scale cloud data centers. Cloud data centers contain thousands of physical machines (PMs), so resource management is necessary for allocating the tremendous amount of data to them. Knowing the workload demand in advance enables control of those resources, saving energy, reducing CPU and memory usage, and improving service. Workload prediction can be used to determine how many resources need to be allocated in the future. In this paper, we propose machine learning–based techniques to predict the daily operational workload. The proposed approach can predict the amount of power consumption (PC) and the number of PMs required to fulfill the demands of the cloud data center. Workload prediction accuracy varies based on the prediction methods used and the type of workload. In this work, we investigate three different methods: polynomial regression, support vector regression, and random forest regression (RFR). Considering both accuracy and computation time, results show that RFR provides the best performance, in our case, with a minimum root-mean-square error of 11.68 for PMs and 4869.08 for PC prediction. The computation time solidifies our selection with 2 seconds training time in all instances.
Developing Cloud Computing Time Slot-availability Predictions Using an Artificial Neural Network
Alanazi Rayan,Muhammad Ashfaq khan,Fawaz Alhazemi,Hamoud Alshammari,Yunmook Nah 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.1
Over the last decade, cloud computing has exponentially transformed the ways of computing. In spite of its various advantages, cloud computing suffers from several challenges that affect performance. Two of the fundamental challenges are power consumption and dynamic resource scaling. An efficient resource allocation strategy could help cloud computing to improve overall performance and operational costs. In this paper, we design a novel approach to available time slot prediction in a data node, based on an artificial neural network (ANN), which predicts the time at which the required resources will be available. We conducted experiments on several nodes, obtaining up to 98%, and outperforming state-of-the-art available time slot prediction approaches. We claim that available time–slot prediction for cloud computing based on an ANN will lead to optimum resource allocation and to minimizing energy consumed while maintaining the essential performance level.
Sentiment Analysis of COVID-19 Tweets: Impact of Pre-processing Step
Ayadi, Rami,Shahin, Osama R.,Ghorbel, Osama,Alanazi, Rayan,Saidi, Anouar International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.3
Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.