Optimizing SAP Workloads on Cloud-Native Platforms: A Framework for Intelligent Resource Allocation and Performance Scaling
Abstract
For the better part of two decades, enterprise capacity management has relied on massive, static over-provisioning to guarantee database stability a legacy mindset that fundamentally clashes with the modern industry infatuation with ephemeral, cloud-native platforms. Contemporary orchestration methodologies attempt to resolve this friction through generic machine learning models that trigger hypervisor-level metrics minutes after a bottleneck has materialized; does wrapping a monolithic database in a container genuinely constitute an architectural advancement? To bridge this foundational disconnect, this research introduces a predictive framework that directly subordinates Kubernetes-driven infrastructure scaling to SAP HANA’s internal memory governance via a Long Short-Term Memory (LSTM) network. By ingesting proprietary internal telemetry to pre-emptively warm memory pools ahead of cyclical analytical swells within an OpenStack-based service cloud, the model effectively neutralizes high-dependency transactional latency and nearly doubles CPU utilization efficiency from 22.36% to 43.02%. While the predictive horizon predictably collapses during unprecedented anomalies exposing the limitations of idealized orchestration algorithms that treat hardware physics as academic toys the framework successfully maintains strict transactional continuity under genuine enterprise duress. Ultimately, this synthesis proves that true elasticity requires abandoning localized infrastructure optimization, establishing a necessary trajectory for future multi-cloud federations to synchronize dynamic provisioning APIs directly with proprietary database constraints.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 1 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
9210-9219 |
Published |
February 15, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sheetal Joyce (%2023). Optimizing SAP Workloads on Cloud-Native Platforms: A Framework for Intelligent Resource Allocation and Performance Scaling. International Journal of Science, Research and Technology , Vol. 6 No. 1 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 9210-9219. https://doi.org/10.15662/IJSRAT.2023.0601002 |
References
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2. Dang-Quang, N.-M., & Yoo, M. (2022). An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing. Applied Sciences. https://doi.org/10.3390/app12073523
3. Chinnam, S. K., & Karanam, R. (2022). AI-Driven Predictive Autoscaling in Kubernetes: Reinforcement Learning for Proactive Resource Optimization in Cloud-Native Environments. International Journal of Scientific Research in Computer Science Engineering and Information Technology. https://doi.org/10.32628/cseit22548
4. Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Autoscaling for Kubernetes Using Multivariate Time-Series Forecasting. International Journal of Emerging Research in Engineering and Technology. https://doi.org/10.63282/3050-922x.ijeret-v2i2p110
5. Sikka, V., Färber, F., Lehner, W., Kyun, S., Peh, T., & Bornhövd, C. (2012). Efficient transaction processing in SAP HANA database. https://doi.org/10.1145/2213836.2213946
6. Lee, J., Kwon, Y. S., Färber, F., Muehle, M., Lee, C.-W., Bensberg, C., Lee, J. Y., Lee, A. H., & Lehner, W. (2013). SAP HANA distributed in-memory database system: Transaction, session, and metadata management. https://doi.org/10.1109/icde.2013.6544906
7. Mao, Y. (2020). Resource Management Schemes for Cloud-Native Platforms with Computing Containers of Docker and Kubernetes. https://doi.org/10.36227/techrxiv.13146548.v1
8. May, N., Lehner, W., Hameed, P. S., Maheshwari, N., Müller, C., Chowdhuri, S., & Goel, A. K. (2015). SAP HANA – From Relational OLAP Database to Big Data Infrastructure. Movebank. https://doi.org/10.5441/002/edbt.2015.59
9. Jambigi, N., Bach, T., Schabernack, F., & Felderer, M. (2022). Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2205.08029
10. Abdullah, M., Iqbal, W., Berral, J. Ll., Polo, J., & Carrera, D. (2020). Burst-Aware Predictive Autoscaling for Containerized Microservices. IEEE Transactions on Services Computing. https://doi.org/10.1109/tsc.2020.2995937
11. Anbalagan, B., Pasumarthi, A., & America, T. S. H. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication. https://doi.org/10.15680/ijctece.2022.0504004
12. Madathala, H., Barmavat, B., Karey, K. S. P., & Balamuralikrishnan, X. (2022). AI-Driven Cost Optimization in SAP Cloud Environments: A Technical Research Paper. International Journal of Science and Research (IJSR). https://doi.org/10.21275/sr241017125233
13. Ruiz, L. M., Pueyo, P. P., Mateo, J., Vilaplana, J., & Solsona, F. (2022). Autoscaling Pods on an On-Premise Kubernetes Infrastructure QoS-Aware. IEEE Access. https://doi.org/10.1109/access.2022.3158743
14. Tahir, F., Abdullah, M., Bukhari, F., Almustafa, K. M., & Iqbal, W. (2020). Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications. IEEE Access. https://doi.org/10.1109/access.2020.2988207
15. Vu, D.-D., Tran, M.-N., & Kim, Y. (2022). Predictive Hybrid Autoscaling for Containerized Applications. IEEE Access. https://doi.org/10.1109/access.2022.3214985
16. Xue, S., Qu, C., Shi, X., Liao, C., Zhu, S., Tan, X., Ma, L., Wang, S., Wang, S., Hu, Y., Lei, L., Zheng, Y., Li, J., & Zhang, J. (2022). A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3539063
17. Cardenas, Y. M. R. (2018). Scaling Policies Derivation for Predictive Autoscaling of Cloud Applications. mediaTUM.
18. Patel, H. R. (2022). SLA-Aware Predictive Autoscaling for Containerized Microservices. European Chemical Bulletin. https://doi.org/10.53555/ecb.v11:i12.14954
19. Psaroudakis, I., Wolf, F., May, N., Neumann, T., Böhm, A., Ailamaki, A., & Sattler, K.-U. (2015). Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling. Lecture notes in computer science. https://doi.org/10.1007/978-3-319-15350-6_7