Elastic Knowledge-based System for Data Analytics on Cloud Computing Platform
Article Sidebar
Main Article Content
Abstract
Knowledge-based system (KBS) is a methodology to carry out an analysis on data having complex interrelationships as it can derive implicit information and knowledge from explicit facts through logical inference process. However, due to the growing number and size of data, the data analysis process spends longer time. This research presents a framework to analyze big data with KBS on cloud computing. Our work aims to shorten the time to analyze big data by distributing KBS processes to multiple cloud computing resources elastically. The system can auto-scale to increase or decrease computing resources to match the workload at each moment. The proposed system is tested by comparing the execution time when using and not using auto-scaling to analyze equipment failure dataset. The results show that the analysis of the dataset is completed faster. The execution time is close to that of 9 virtual machines while the cost is similar to that of 2 virtual machines, achieving the best price-performance ratio.
Article Details
References
C. Chen, S. Chen, F. Yin, and W. Wang. “Efficient Hybriding Auto-scaling for OpenStack Platforms.” 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, pp. 1079-1085, 2015.
J. Bao, Z. Lu, J. Wu, S. Zhang, and Y. Zhong. “Implementing a Novel Load-aware Auto Scale Scheme for Private Cloud Resource Management Platform.” 2014 IEEE Network Operations and Management Symposium (NOMS), Krakow, pp. 1-4, 2014.
A. A. D. P. Souza, and M. A. S. Netto. “Using Application Data for SLA-Aware Auto-scaling in Cloud Environments.” 2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Atlanta, GA, pp. 252-255, 2015.
M. Wang, D. Zhang, and B. Wu. “A Cluster Autos Based on Multiple Node Types in Kubernetes.” 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, pp. 575-579, 2020.
Kubernetes Cluster Autoscaler, Google Kubernetes Engine’s Cluster Autoscaler. Available Online at https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler, accessed on 1 September 2022.
Amazon Web Services, AWS ParallelCluster Auto Scaling. Available Online at https://docs.aws.amazon. com/parallelcluster/latest/ug/autoscaling.html, accessed on 1 September 2022.
A. Luckow, I. Paraskevakos, G. Chantzialexiou, and S. Jha. “Hadoop on HPC: Integrating Hadoop and Pilot-Based Dynamic Resource Management.” 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016.
S. L. Kendal, and M. Creen. An Introduction to Knowledge Engineering. Springer, 2007.
S. J. Russell, and P. Norvig. “Artificial Intelligence A Modern Approach”, Fourth Edition, pp. 356, Pearson, 2022.
K. Jahr, and A. Borrmann. “Semi-automated Site Equipment Selection and Configuration through Formal Knowledge Representation and Inference.” Advanced Engineering Informatics, Vol. 38, pp. 488-500, 2018.
M. Ju, and Q. Qian. “Materials Knowledge Reasoning with Production based System.” Computational Materials Science, Vol. 173, February 2020.
M. Li, W. Lu, D. Xiang, and Z. Wen. “Design and Realization of Transformer Fault Diagnostic Expert System Based on Drools.” 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015.
Slurm Workload Manager. Available Online at https://slurm.schedmd.com/. accessed on 1 September 2022.
OpenStack: Open Source Cloud Computing Infrastructure. Available Online at https://www. openstack.org. accessed on 1 September 2022.
Drools. Available Online at https://www.drools.org/. accessed on 1 September 2022.
A. Fobel, and N. Subramanian. “Comparison of the Performance of Drools and Jena Rule-based Systems for Event Processing on the Semantic Web.” 2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), 2016.
LINPACK Benchmark. Available Online at https://netlib.org/benchmark/. accessed on 1 September 2022.
N. R. Milton. Knowledge Acquisition in Practice A Step-by-step Guide. Springer, 2007.