SCALABLE COMPLEX EVENT PROCESSING USING RULE DISTRIBUTION
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Volume 1 (2), December 2018, Pages 133-139
Mohsen Sharifi, Mohammad Ali Fardbastani
Distributed Systems Research Lab, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Complex event processing (CEP) systems are currently widely used in large-scale enterprises for the processing of high and dynamically changing rates of input events using large number of complex rules. Given the hardware limitations of vertically scaled CEP solutions, horizontal scalability has become an essential requirement for modern CEP systems. In this paper, we propose an adaptive load-balancing technique via rule distribution (called ARD) for a cluster of CEP engines that provides horizontal scalability for CEP systems. Our experiments show our proposed technique provides higher scalability and yields higher throughput in comparison with two previously proposed non-adaptive load-balancing techniques, namely VISIRI and SCTXPF, when the system faces with variable workload. In addition, ARD keeps the system balanced more often.
Keywords:
complex event processing, scalability, CEP, horizontal scaling, load balancing, throughput.
DOI: https://doi.org/10.32010/26166127.2018.1.2.133.139
Reference
[1] Dayarathna, M., & Perera, S. (2018). Recent Advancements in Event Processing. ACM Computing Surveys, 51(2), 1-36. doi:10.1145/3170432
[2] Zhang, P., Shi, X., & Khan, S. U. (2018). Quantcloud: Enabling big data complex event processing for quantitative finance through a data-driven execution. IEEE Transactions on Big Data.
[3] Shi, S., Jin, D., & Tiong-Thye, G. (2017). Real-time public mood tracking of Chinese microblog streams with complex event processing. IEEE Access, 5, 421-431.
[4] Fardbastani, M. A., Allahdadi, F., & Sharifi, M. (2018). Business process monitoring via decentralized complex event processing. Enterprise Information Systems, 12(10), 1257-1284.
[5] Bonino, D., & De Russis, L. (2018). Complex Event Processing for City Officers: A Filter and Pipe Visual Approach. IEEE Internet of Things Journal, 5(2), 775-783.
[6] Graubner, P., Thelen, C., Körber, M., Sterz, A., Salvaneschi, G., Mezini, M., Seegar, B., Freisleben, B. (2018, June). Multimodal Complex Event Processing on Mobile Devices. In Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems (pp. 112-123). ACM.
[7] Browne, P. (2009). JBoss drools business rules. Birmingham: Packt.
[8] Isoyama, K., Kobayashi, Y., Sato, T., Kida, K., Yoshida, M., & Tagato, H. (2012, July). A scalable complex event processing system and evaluations of its performance. In Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems (pp. 123-126). ACM.
[9] Kumarasinghe, M., Tharanga, G., Weerasinghe, L., Wickramarathna, U., & Ranathunga, S. (2015, June). VISIRI-Distributed Complex Event Processing System for Handling Large Number of Queries. In International Conference on Coordination Languages and Models (pp. 230-245). Springer, Cham.
[10] Fathollahzadeh, S., Teymourian, K., & Sharifi, M. (2016, June). Stateful complex event detection on event streams using parallelization of event stream aggregations and detection tasks. In Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (pp. 390-393). ACM.
[11] Mayer, R., Slo, A., Tariq, M. A., Rothermel, K., Gräber, M., & Ramachandran, U. (2017, December). Spectre: Supporting consumption policies in window-based parallel complex event processing. In Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference (pp. 161-173). ACM.
[12] Mayer, R., Tariq, M. A., & Rothermel, K. (2017, June). Minimizing communication overhead in window-based parallel complex event processing. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (pp. 54-65). ACM.
[13] Gong, Y., Kuang, H., Cai, X., Hu, H., Song, W., & Lu, J. (2017, June). Parallelized Mobility-Aware Complex Event Processing. In 2017 IEEE International Conference on Web Services (ICWS) (pp. 898-901). IEEE.
[14] Dwarakanath, R. C., Koldehofe, B., & Steinmetz, R. (2016, December). Operator Migration for Distributed Complex Event Processing in Device-to-Device Based Networks. In M4IoT Middleware (pp. 13-18).
[15] Fonseca, J., Ferraz, C., & Gama, K. (2016, June). A policy-based coordination architecture for distributed complex event processing in the internet of things: doctoral symposium. In Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (pp. 418-421). ACM.
[16] Weisenburger, P., Luthra, M., Koldehofe, B., & Salvaneschi, G. (2017, May). Quality-aware runtime adaptation in complex event processing. In Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017 IEEE/ACM 12th International Symposium on (pp. 140-151). IEEE.
[17] Wang, Q., & Shang, Y. (2019). A Distributed Complex Event Processing System Based on Publish/Subscribe. In Recent Developments in Intelligent Computing, Communication and Devices (pp. 981-990). Springer, Singapore.
[18] Kobayashi, Y., Isoyama, K., Kida, K., & Tagato, H. (2015, June). A complex event processing for large-scale M2M services and its performance evaluations. In Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (pp. 336-339). ACM.
[19] Pathak, R., & Vaidehi, V. (2015, May). An efficient rule balancing for scalable complex event processing. In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 190-195).
