Performance analysis of intelligent agents in complex event processing systems

• Autorzy: Walkowiak Tomasz , Mazurkiewicz Jacek , Sugier Jarosław , Śliwiński Przemysław

Identyfikatory

DOI

10.26408/srsp-2022-14

• Konferencja: complex event processing system intelligent agent deep learning performance analysis
• Języki publikacji: EN

Abstrakty

EN

The chapter discusses the performance aspects of intelligent agents in Complex Event Processing (CEP) systems. The contemporary solution for implementing CEP systems is based on available software components (Siddhi) and modern implementation techniques (Kubernetes). However, Siddhi lacks the implementation of modern deep learning algorithms. Hence, the concept of intelligent agent is introduced. A case study with a set of intelligent agents designed to handle real-world events related to environmental data monitoring is presented. The results of the case study discussion indicate a reasonable scale for tuning the Event Processing Element (EPA) topology with correct responses and the required output performance level. These results have important implications for the practical implementation of the EPA structure, i.e., the use of GPUs in CEP systems. Finally, the results of performance analysis of different implementations of intelligent agents are presented and discussed.

Słowa kluczowe

EN

complex event processing system; intelligent agent; deep learning; performance analysis

• Wydawca: Gdynia Maritime University ; Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Safety and Reliability of Systems and Processes
• Rocznik: 2022
• Tom: Summer Safety and Reliability Seminar 2022
• Strony: 195--204
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Walkowiak Tomasz

• Wrocław University of Science and Technology, Wrocław, Poland

• https://orcid.org/0000-0002-7749-4251

autor

Mazurkiewicz Jacek

• Wrocław University of Science and Technology, Wrocław, Poland

• https://orcid.org/0000-0002-7708-907X

autor

Sugier Jarosław

• Wrocław University of Science and Technology, Wrocław, Poland

• https://orcid.org/0000-0003-1452-3067

autor

Śliwiński Przemysław

• Wrocław University of Science and Technology, Wrocław, Poland

• https://orcid.org/0000-0003-3839-1580

Bibliografia

• Abhishek, K., Singh, M.P., Ghosh, S.&Anand, A. 2012. Weather forecasting model using artificial neural network. Procedia Technology4, 311-318.
• Adamczak, R., Duch, W.& Jankowski, N. 1997. New developments in the feature space mapping model. Third Conference on Neural Networks and Their Applications. Kule, Poland, 65-70.
• Bai, J., Lu, F., Zhang, K. et al. 2022. ONNX: Open Neural Network Exchange. GitHub https://github.com/onnx/onnxi.(accessed01June 2022).
• Bonarini, A., Masulli, F. & Pasi, G. 2003. Advances in Soft Computing, Soft Computing Applications. Springer.
• Devlin, J., Chang, M-W., Lee, K.& Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/arXiv.1810.04805.
• Duch, W.& Diercksen, G.H.F. 1994. Feature space mapping as a universal adaptive system. Computer Physics Communications 87, 341-371.
• Duch, W., Jankowski, N., Naud, A. & Adamczak, R. 1995. Feature space mapping: a neurofuzzy network for system identification. Proceedings of the European Symposium on Artificial Neural Networks. Helsinki, 221-224.
• Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R.& Schmidhuber, J. 2017. LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems 28(10), 2222-2232.
• Hastie, T., Friedman, J. & Tisbshirani, R. 2018. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, Springer.
• Hinton, G.E. 1987. Learning translation invariant recognition in massively parallel networks. Proceedings PARLE Conference on Parallel Architectures and Languages Europe. Berlin. Springer-Verlag, 1-13.
• Huang, Z., Xu P., Liang, D., Mishra, A. &Xiang, B. 2020. TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding. https://doi.org/10.48550/arXiv.2003.07000.
• Kubernetes. 2022. https://kubernetes.io/(accessed 01June2022).
• Kung, S.Y. 1993. Digital Neural Networks. Prentice-Hall.
• Le Cun, Y., Denker, J.& Solla, S. 1990. Optimal Brain Damage. Advances in Neural Information Processing Systems 2. Morgan Kauffman. San Mateo CA.
• Limnios, N.& Oprisan, G. 2001. Semi-Markov Processes and Reliability. Birkhauser, Boston.
• Merkel, D. 2014. Docker: lightweight Linux containers for consistent development and deployment. Linux Journal 2014(239), 2.
• Microsoft. 2022. ONNX Runtime, https://github.com/microsoft/onnxruntime(accessed 01June2022).
• Nats. 2022. NATS.io; Cloud Native, Open Source, High-performance Messaging, https://nats.io/,https://nats.io/documentation/(accessed 01June2022).
• Peter, M.E., Neumann,M., Iyyer, M., Gardner,M., Clark, Ch., Lee, K.& Zettlemoyer, L. 2018. Deep contextualized word representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies1, 2227-2237, New Orleans, Louisiana. Association for Computational Linguistics.
• Reimers, N.& Gurevych, I. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.3973-3983. 10.18653/v1/D19-1410.
• Srivastava, A.K. 2008. Soft Computing. Narosa PH.
• Sugier, J., Walkowiak, T., Mazurkiewicz, J., Śliwiński, P. & Helt, K. 2019. Performance evaluation of event-driven software applied in monitoring systems. I. Kabashkin, I.Yatskiv (Jackiva), O.Prentkovskis (Eds.).Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems 68. Springer, Cham, 311-319.
• Suhothayan, S., Gajasinghe, K., Narangoda, I.L., Chaturanga, S., Perera, S.& Nanayakkara, V. 2011. Siddhi: a second look at complex event processing architectures. Proceedings of the 2011 ACM workshop on Gateway computing environments (GCE'11). Association for Computing Machinery, New York, NY, USA, 43-50.
• Weigend, A.S., Rumelhart, D.E.& Huberman, B.A. 1990. Backpropagation, weight elimination and time series prediction. Proceedings of the 1990 Connectionist Models Summer School. Morgan Kaufmann, 65-80.
• Weigend, A.S., Rumelhart, D.E.& Huberman, B. A. 1991. Generalization by weight elimination with application to forecasting. Advances in Neural Information Processing Systems 3. San Mateo CA. Morgan Kaufmann, 875-882.
• Wolf, T., Debut, L. et al. 2020. Transformers: state-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38-45, Online. Association for Computational Linguistics.
• Wolff, E. 2016. Microservices: Flexible Software Architectures. Addison-Wesley.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).