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Cloud Data Center Resource Demand Prediction Model Development on Apache Spark
http://hdl.handle.net/20.500.12678/0000003370
http://hdl.handle.net/20.500.12678/0000003370951e49cc-6d88-4e24-8505-9f9a93bf41e1
e596f5ea-894f-4a0d-b7ed-d72de6a8a868
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ICCA 2019 Proceedings Book-pages-41-47.pdf (332 Kb)
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Cloud Data Center Resource Demand Prediction Model Development on Apache Spark | |||||
Language | en | |||||
Publication date | 2019-02-27 | |||||
Authors | ||||||
Than, Moh Moh | ||||||
Thein, Thandar | ||||||
Description | ||||||
Dynamic resource allocation in cloud datacenters is a challenging problem. Resourceprediction is a key feature for on-demand resourceplanning and efficient resource management ofdynamic workload. This requires a highly accuratedemand prediction. Hyper-parameter optimizationcan largely affect the performance of the predictionmodel. The process of identifying the optimalparameters for a machine learning (ML) algorithminvolves the search for a broad range of valuecombinations of parameter sets. This paper presentsa resource demand prediction model with the cloudcomputational frameworks Apache™ Hadoop® andApache Spark™. The model is developed on thepowerful ML technique, Decision Tree (DT)algorithm, and hyper-parameter optimization for DTalgorithm is performed to achieve the predictionmodel with high accuracy. The evaluation ofprediction model is conducted on real data centerworkload traces and the evaluation results show thathyper-parameter optimization can save theprediction error significantly. | ||||||
Keywords | ||||||
Apache Hadoop, Apache Spark, Hyperparameter Optimization, Machine Learning, Resource Prediction | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/1128 | |||||
Journal articles | ||||||
Seventeenth International Conference on Computer Applications(ICCA 2019) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |