SAP Failover Setup for HANA Database 1.0 SP12 and SAP ASCS/ERS using Pacemaker Tool

Main Article Content

Harikrishna Madathala

Abstract

This research paper explores the implementation of a high availability solution for SAP HANA Database 1.0 SP12 and SAP ASCS/ERS (ABAP Central Services/Enqueue Replication Server) using the Pacemaker clustering tool. The study focuses on the architecture, configuration, and best practices for setting up a robust failover mechanism to ensure continuous operation of critical SAP systems. By examining the integration of SAP HANA's system replication capabilities with Pacemaker's cluster management features, this paper provides insights into creating a resilient SAP environment capable of minimizing downtime and maintaining data consistency during failover events.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Madathala, H. . (2019). SAP Failover Setup for HANA Database 1.0 SP12 and SAP ASCS/ERS using Pacemaker Tool. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(1), 753–787. https://doi.org/10.61841/turcomat.v10i1.14805
Section
Research Articles

References

Bartolini, C., Giurgiu, I., Tauber, M., & Wiesmann, M. (2017). Automated Application Recovery in the Cloud: Empirical Evaluation of SAP HANA on AWS. In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD) (pp. 252-259). IEEE.

Beine, M., Herschel, R., & Castellanos, M. (2016). Enabling Real-Time Business Intelligence on SAP HANA. In Data Management Technologies and Applications, 201-221. Springer.

Bog, A., & Plattner, H. (2015). Interactive performance management for SAP HANA. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 1119-1124.

Cheng, B., & Zhao, S. (2017). Pacemaker-based High Availability Cluster Implementation in OpenStack. In Proceedings of the International Conference on Computer Network, Electronic and Automation (ICCNEA), 164-168.

Daidone, A., Di Giandomenico, F., Chiaradonna, S., & Bondavalli, A. (2018). Hidden Markov Models as a Support for Diagnosis: Formalization of the Problem and Synthesis of the Solution. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (pp. 1-12). IEEE.

Daidone, A., Di Giandomenico, F., Chiaradonna, S., & Bondavalli, A. (2018). Hidden Markov Models as a Support for Diagnosis: Formalization of the Problem and Synthesis of the Solution. In Proceedings of the 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 1-12.

Färber, F., Cha, S. K., Primsch, J., Bornhövd, C., Sigg, S., & Lehner, W. (2015). SAP HANA database: data management for modern business applications. ACM Sigmod Record, 40(4), 45-51.

Hasso Plattner Institute. (2016). In-Memory Data Management Technology. Springer-Verlag Berlin Heidelberg.

Kemper, A., & Neumann, T. (2014). HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots. In Proceedings of the 27th IEEE International Conference on Data Engineering, 195-206.

Krüger, J., Grund, M., Zeier, A., & Plattner, H. (2015). Enterprise application-specific data management. In Proceedings of the 14th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2010) (pp. 131-140). IEEE.

Lee, J., Kim, Y. S., & Kim, H. (2017). Failure prediction for autonomic live migration for high availability. Cluster Computing, 20(1), 587-599.

Liang, F., Feng, C., Lu, X., & Xu, Z. (2017). Performance benefits of DataNucleus over Hibernate for object-relational mapping. Future Generation Computer Systems, 76, 1-13.

Makris, A., Tserpes, K., Spiliopoulos, G., & Anagnostopoulos, D. (2018). Performance Evaluation of MongoDB and PostgreSQL for spatio-temporal data. In EDBT/ICDT Workshops.

Márton, G., & Varró, D. (2016). Formalizing and automating domain-specific refactorings. Software & Systems Modeling, 15(4), 1201-1222..

Mühe, H., Kemper, A., & Neumann, T. (2018). Executing long-running transactions in synchronization-free main memory database systems. In Proceedings of the 2018 International Conference on Management of Data, 135-148.

Plattner, H. (2014). The impact of columnar in-memory databases on enterprise systems: implications of eliminating transaction-maintained aggregates. Proceedings of the VLDB Endowment, 7(13), 1722-1729.

Plattner, H., & Zeier, A. (2016). In-Memory Data Management Technology. Springer-Verlag Berlin Heidelberg.

Qu, L., Wang, Y., & Orgun, M. A. (2018). Cloud service selection based on the aggregation of user feedback and quantitative performance assessment. In 2013 IEEE International Conference on Services Computing (pp. 152-159). IEEE.

SAP SE. (2017). SAP HANA Administration Guide for SAP HANA Platform 2.0 SPS 02. SAP Help Portal.

Schaffner, J., Bog, A., Krüger, J., & Zeier, A. (2015). A hybrid row-column OLTP database architecture for operational reporting. In Business Intelligence for the Real-Time Enterprise (pp. 61-74). Springer, Berlin, Heidelberg.

Sharma, Y., Javadi, B., Si, W., & Sun, D. (2016). Reliability and energy efficiency in cloud computing systems: Survey and taxonomy. Journal of Network and Computer Applications, 74, 66-85.

Silberschatz, A., Korth, H. F., & Sudarshan, S. (2016). Database system concepts. McGraw-Hill Education.

Singh, A., & Singla, A. R. (2015). Performance optimization of fault-tolerant pacemaker cluster. International Journal of Computer Applications, 115(16), 34-38.

Stonebraker, M., & Cetintemel, U. (2015). "One size fits all": an idea whose time has come and gone. In Making Databases Work: the Pragmatic Wisdom of Michael Stonebraker (pp. 441-462).

Tan, Z., & Babu, S. (2016). Tempo: robust and self-tuning resource management in multi-tenant parallel databases. Proceedings of the VLDB Endowment, 9(10), 720-731.

Willhalm, T., Popovici, N., Boshmaf, Y., Plattner, H., Zeier, A., & Schaffner, J. (2015). SIMD-scan: ultra fast in-memory table scan using on-chip vector processing units. Proceedings of the VLDB Endowment, 2(1), 385-394.

Zhang, Q., Cheng, L., & Boutaba, R. (2015). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7-18.