User Behavior and Application Modeling in Decentralized Edge Cloud Infrastructures
Edge computing has emerged as a solution that can accommodate complex application requirements by shifting data and computation to infrastructure elements that are more suitable to manage them given the current circumstances. The BASMATI Knowledge Extractor is a component that facilitates the modeling of the resource utilization by providing tools to analyze application usage together with user behavior. This is particularly relevant in the case of mobile applications where user context and activity are tightly coupled to the application performance.
KeywordsPerformance modeling Resource utilization User behavior modeling Application usage modeling BASMATI project
BASMATI (http://basmati.cloud) has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement no. 723131 and from ICT R&D program of Korean Ministry of Science, ICT and Future Planning no. R0115-16-0001.
- 1.Deeplearning4j: Open-source distributed deep learning for the jvm. https://deeplearning4j.org. Accessed 17 July 17
- 2.Aisopos, F., Tzannetos, D., Violos, J., Varvarigou, T.A.: Using n-gram graphs for sentiment analysis: an extended study on twitter. In: Second IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016, Oxford, United Kingdom, March 29 - April 1, 2016, pp. 44–51 (2016). http://dx.doi.org/10.1109/BigDataService.2016.13
- 3.Dutt, S.: New faster kernighan-lin-type graph-partitioning algorithms. In: Proceedings of 1993 International Conference on Computer Aided Design (ICCAD), pp. 370–377, November 1993Google Scholar
- 7.John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, UAI 1995, pp. 338–345. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
- 16.Violos, J., Tserpes, K., Papaoikonomou, A., Kardara, M., Varvarigou, T.A.: Clustering documents using the 3-gram graph representation model. In: 18th Panhellenic Conference on Informatics, PCI 2014, Athens, Greece, October 2–4, 2014, pp. 29:1–29:5 (2014). http://doi.acm.org/10.1145/2645791.2645812
- 17.Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: PractIcal Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)Google Scholar