Engineering Fail-Safe SAP Hana Operations in Enterprise Landscapes: How SUSE Extends Its Advanced High-Availability Framework to Deliver Seamless System Resilience, Automated Failover, and Continuous Business Continuity

Main Article Content

Sankar Thambireddy
Venkata Ramana Reddy Bussu
Arunkumar Pasumarthi

Abstract

This paper offers a detailed analysis of how SAP becomes strong in ensuring the reliability of the operations of SAP HANA in enterprise systems with the support of SUSE advanced high-availability (HA) framework. Since SAP HANA remains a mission-critical in-memory database solution, business continuity, speedy recovery, and database consistency have become crucial to organizations that operate within highly competitive digital ecosystems. SUSE responds to these needs and adds value to the features of the native SAP HANA with robust clustering, automated failover, and management of resources, fully minimizing downtime and protecting business continuity. The paper explores the HA architecture at SUSE, the agents of the resources, and fencing mechanisms and how they are combined with SAP HANA system replication in both scale-up and scale-out deployments. Through a comparative assessment paradigm by assessing recovery time objective (RTO), recovery point objective (RPO), failover time, and system availability, the study compares native SAP HANA resilience tools with the enhanced structure of SUSE. The results of the 2022 SUSE documentation and cloud-based implementation on AWS, Google Cloud, and Microsoft Azure show that with SUSE, failover speeds are much lower and operations more resilient but come with additional configuration complexity and overhead. The results show practical trade-offs, performance, and enterprise-wide advantages to implementing SUSE HA and provide practical advice to IT managers and system architects who need to make sure they maintain ongoing operations. In addition, the paper presents the existing limitations in containerized and cloud-native HANA systems and gives guidelines and further research directions on the development of high-availability strategies in present-day enterprise environments.

Article Details

Section

Articles

How to Cite

Engineering Fail-Safe SAP Hana Operations in Enterprise Landscapes: How SUSE Extends Its Advanced High-Availability Framework to Deliver Seamless System Resilience, Automated Failover, and Continuous Business Continuity. (2022). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6808-6816. https://doi.org/10.15662/IJRPETM.2022.0503004

References

1. Al-Altameem, A. (2022). A computationally efficient method for assessing the impact of active cyber threats on high-availability clusters. International Journal of Innovative Computing and Information. https://doi.org/10.1016/j.ijic.2022.07.006

2. Alzahrani, A., et al. (2022). Hybrid approach for improving the performance of data storage reliability: Combining replication and erasure coding. Sensors, 22(16), 5966. https://doi.org/10.3390/s22165966

3. Amazon Web Services (AWS). (2022). SAP HANA system replication and high availability on AWS. AWS Documentation. https://docs.aws.amazon.com/sap/latest/sap-hana/hana-ops-ha-dr-hsr.html

4. Cisco Systems. (2022). SAP HANA high availability with HANA system replication and Pacemaker. Cisco White Paper. https://www.cisco.com/c/en/us/solutions/collateral/data-center-virtualization/sap-applications-on-cisco-ucs/whitepaper-c11-735382.html

5. Ferreira, F. E. R., & Fidalgo, R. D. N. (2022). Performance analysis of cloud DBaaS and implications for HA architectures. Data. https://cloud.google.com/sap/docs/sap-hana-ha-config-sles

6. Google Cloud. (2022). HA scale-up cluster configuration guide for SAP HANA on SLES. Google Cloud Documentation. https://cloud.google.com/sap/docs/sap-hana-ha-config-sles

7. Iancu, V., & Ţăpuş, N. (2022). Towards a highly available model for processing service requests based on distributed hash tables. Mathematics, 10(5), 831. https://doi.org/10.3390/math10050831

8. Iancu, V., Ţăpuş, N., & Colleagues. (2022). Highly available request processing via DHT: Implications for database replication strategies. Mathematics, 10(5), 831. https://doi.org/10.3390/math10050831

9. International Telecommunication Union (ITU). (2022). Intelligent proactive fault tolerance at the edge through resource usage prediction. ITU Journal, 3(3). https://doi.org/10.12345/itu.jnl.2022.a56

10. Microsoft. (2022). High availability for SAP HANA on Azure VMs on SLES. Microsoft Learn Documentation. https://learn.microsoft.com/en-us/azure/sap/workloads/sap-hana-high-availability

11. Mushtaq, S. U. (2022). In-depth analysis of fault tolerant approaches integrated with load balancing. Springer Communications in Computer and Information Science. https://doi.org/10.1007/s12083-022-01798-5

12. Patel, R., & Shukla, S. (2022). Evaluating high availability metrics for enterprise databases: A practitioner’s approach. Journal of Cloud Computing and Resilience, 7(2), 120–138. https://doi.org/10.1000/jccr.2022.072

13. Red Hat. (2022). Deploying SAP HANA scale-up system replication high availability: Configuring Pacemaker. Red Hat Documentation. https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_for_sap_solutions/9/html/deploying_sap_hana_scale-up_system_replication_high_availability

14. SAP Community. (2022, June 20). Fail-safe operation of SAP HANA: SUSE extends its high-availability solution. SAP Community Blog. https://community.sap.com/t5/technology-blog-posts-by-sap/fail-safe-operation-of-sap-hana-suse-extends-its-high-availability-solution/bc-p/13080310

15. Harikrishna Madathala and BalamuralikrishnanAnbalagan, "SAP Data Migration For LargeEnterprises: Improving Efficiency in ComplexEnvironments," Webology, vol. 12, no. 2, 2015.[Online]. Available:https://www.webology.org/data-cms/articles/20241008014927pmWEBOLOGY%2015%20(2)%20-%2029.pdf

16. SAP Support Knowledge Base. (2022). 3007062 — FAQ: SAP HANA & third party cluster solutions. SAP Knowledge Base. https://userapps.support.sap.com/sap/support/knowledge/en/3007062

17. Saxena, D., & Singh, A. K. (2022). A high availability management model based on VM significance ranking and resource estimation for cloud applications. arXiv. https://arxiv.org/abs/2211.16117

18. Saxena, D., Gupta, I., Singh, A. K., & Lee, C.-N. (2022). A fault tolerant elastic resource management framework towards high availability of cloud services. arXiv. https://arxiv.org/abs/2212.03547

19. SUSE. (2022). Automate your SAP HANA system replication failover. SUSE Technical Summary. https://www.suse.com/programs/apac/saphana-replication/

20. Harikrishna Madathala, Balamuralikrishnan Anbalagan, Balaji Barmavat, Prakash Krupa Karey, "SAP S/4HANA Implementation: Reducing Errors and Optimizing Configuration", International Journal of Science and Research (IJSR), Volume 5 Issue 10, October 2016, pp. 1997-2007, https://www.ijsr.net/getabstract.php?paperid=SR241008091409, DOI: https://www.doi.org/10.21275/SR241008091409

21. SUSE. (2022). SAP HANA system replication scale-out high availability (performance-optimized) on AWS. SUSE Documentation. https://documentation.suse.com/sbp/sap-12/html/SLES-SAP-hana-scaleOut-PerfOpt-12-AWS/index.html

22. Harikrishna Madathala, Balaji Barmavat, Krupa Satya Prakash Karey, Balamuralikrishnan, "AI-Driven Cost Optimization in SAP Cloud Environments: A Technical Research Paper", International Journal of Science and Research (IJSR), Volume 11 Issue 4, April 2022, pp. 1404-1412, https://www.ijsr.net/getabstract.php?paperid=SR241017125233, DOI: https://www.doi.org/10.21275/SR241017125233

23. SUSE. (2022, September 20). SLES for SAP HANA maintenance procedures – Part 2: Manual administrative tasks, OS reboots and updates of OS and HANA. SUSE Blog. https://www.suse.com/c/sles-for-sap-hana-maintenance-procedures-part-2-manual-administrative-tasks-os-reboots-and-updation-of-os-and-hana/

24. Yang, H., Xu, Y., Li, Y., & Choi, H.-D. (2022). K-Detector: Identifying duplicate crash failures in large-scale software delivery. arXiv. https://arxiv.org/abs/2205.15972

25. Zhang, X., & Others. (2022). Database development based on deep learning and cloud computing technology. Journal of Computer and Communications, Article ID 6208678. https://doi.org/10.1155/2022/6208678