HYBRID METHOD FOR HORIZONTAL AND VERTICAL COMPUTATIONAL RESOURCE SCALING
DOI:
https://doi.org/10.17721/AIT.2024.1.05Keywords:
information technologies, information systems, resource management, scaling, IT-infrastructure, vertical scaling, horizontal scaling.Abstract
B a c k g r o u n d . Increasing the efficiency of cluster computing resources while maintaining the established QoS levels is a critical task in IT infrastructure management. Dynamic management of computing resources, in particular vertical and horizontal scaling, are tools that allow automating the processes of adapting applications to dynamic loads. The aim of the work is to improve the efficiency of existing scaling methods by combining them. M e t h o d s . A hybrid scaling method using the coordination module is proposed. This module coordinates the operation of vertical and horizontal scaling components based on the given constraints, priorities, and the current state of the system. The coordination module aims to increase efficiency of the both components and prevents inconsistency in the combination of the number of instances and resource requests for each instance. R e s u l t s . To achieve the given objective, a hybrid method of vertical and horizontal scaling using priority-based component coordination was developed. Priority configuration affects the order of components operation. In the case of horizontal-vertical order, the non-prior vertical component does not affect configuration of the prior horizontal component. The developed method is evaluated based on modeling the operation of an application with a load containing a constant seasonality. The experiments demonstrate a 65% reduction in the unprofitable reservation of cluster computing resources compared to static requests. C o n c l u s i o n s . The developed method can be used to increase the efficiency of resource utilization in clusters under dynamic loads compared to basic scaling methods. In further research, it is necessary to evaluate the method using real infrastructure. It is also necessary to investigate the work of a hybrid method using a predictive approachDownloads
References
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2017). Elasticity in cloud computing: State of the art and research challenges. IEEE Transactions on Services Computing, 11(2), 430–447. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TSC.2017.2711009
Al-Haidari, F., Sqalli, M. H., & Salah, K. (2013). Impact of CPU utilization thresholds and scaling size on autoscaling cloud resources. IEEE 5th International
. In I. Foster, E. Feig, & S. Yau (Eds.) Conference on Cloud Computing Technology and Science, 2 (pp. 256–261). Institute of Electrical and Electronics Engineers. Calheiros, R. N., Toosi, A. N., Vecchiola, C., & Buyya, R. (2012). A coordinator for scaling elastic applications across multiple clouds. Future Generation Computer Systems, 28(8), 1350–1362. https://doi.org/10.1016/j.future.2012.03.010
Dutta, S., Gera, S., Verma, A., & Viswanathan, B. (2012). SmartScale: Automatic Application Scaling in Enterprise Clouds. In I. Foster, E. Feig, & S. Yau (Eds.). IEEE 5th International Conference on Cloud Computing (pp. 221–228). IEEE. https://doi.org/10.1109/cloud.2012.12
Incerto, E., Tribastone, M., & Trubiani, C. (2018). Combined Vertical and Horizontal Autoscaling Through Model Predictive Control. In M. Aldinucci, L. Padovani, & M. Torquati (Eds.). Lecture Notes in Computer Science. Vol. 11014. Parallel Processing (pp. 147–159). Springer International Publishing. https://doi.org/10.1007/978-3-319-96983-1_11
Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. Journal of Grid Computing, 12(4), 559–592. https://doi.org/10.1007/s10723-014-9314-7
Millnert, V., & Eker, J. (2020). HoloScale: Horizontal and vertical scaling of cloud resources. In N. Antonopoulos, O. Rana, & Ch. Jiang (Eds.) 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), 55 (pp. 196–205). IEEE. https://doi.org/10.1109/ucc48980.2020.00038
Omelchenko, V., & Rolik, O. (2022). Automation of resource management in information systems based on reactive vertical scaling. Adaptive systems of automatic control, 2(41), 65–78. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”. https://doi.org/10.20535/1560-8956.41.2022.271344
Qu, C., Calheiros, R. N., & Buyya, R. (2018). Auto-scaling web applications in clouds. ACM Computing Surveys, 51(4), 1–33. https://doi.org/10.1145/3148149
Quattrocchi, G., Incerto, E., Pinciroli, R., Trubiani, C., & Baresi, L. (2024). Autoscaling Solutions for Cloud Applications Under Dynamic Workloads. In IEEE Transactions on Services Computing, 17(3), 804–820. IEEE. https://doi.org/10.1109/tsc.2024.3354062
Rodriguez, M. A., & Buyya, R. (2018). Container-based cluster orchestration systems: A taxonomy and future directions. Software: Practice and Experiencee,49(5), 698–719. https://doi.org/10.1002/spe.2660
Rochman, Y., Levy, H., & Brosh, E. (2014). Efficient resource placement in cloud computing and network applications. SIGMETRICS Performance Evaluation Review, 42(2), 49–51. https://doi.org/10.1145/2667522.2667538
Rolik, O., Telenik, S., & Yasochka, M. (2018). Enterprise IT-infrastructure management. Naukova dumka [in Ukrainian].
Rolik, O., & Omelchenko, V. (2024). Proactive horizontal scaling method for Kubernetes. Radio Electronics, Computer Science, Control, 1, 221–227. National University “Zaporizhzhia Polytechnic”. https://doi.org/10.15588/1607-3274-2024-1-20
Sedaghat, M., Hernandez-Rodriguez, F., & Elmroth, E. (2013). A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling. In S. Hariri, & A. Sill (Eds.). 2013 ACM Cloud and Autonomic Computing Conference (pp. 1–10). ACM. https://doi.org/10.1145/2494621.2494628
Singh, P., Gupta, P., Jyoti, K., & Nayyar, A. (2019). Research on auto-scaling of web applications in cloud: Survey, trends, and future directions. Scalable Computing: Practice and Experience, 20(2), 399–432. https://doi.org/10.12694/scpe.v20i2.1537
Straesser, M., Grohmann, J., von Kistowski, J., Eismann, S., Bauer, A., & Kounev, S. (2022). Why is it not solved yet? In D. Feng, & S. Becker (Eds.). 2022 ACM/SPEC International Conference on Performance Engineering (pp. 105–115). ACM. https://doi.org/10.1145/3489525.3511680
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Advanced Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License
