Optimizing Performance in SAP Success Factors Learning with Efficient Customization Updates and Caching Mechanisms

Authors: Pradeep Kumar

DOI: https://doi.org/10.5281/zenodo.14607728

Short DOI: https://doi.org/g8xxn3

Country: USA

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Abstract: SAP SuccessFactors Learning (SF Learning), a component of SAP’s Human Capital Management (HCM) suite, is vital in facilitating and managing corporate learning initiatives. The system's architecture, which is based on Java Virtual Machine (JVM) and Apache Tomcat, traditionally processes client requests by checking for any customer-specific customization updates in real-time. This approach involves substantial performance overhead as the system continually verifies each customization file’s last updated timestamp across potentially thousands of files per server, leading to high CPU usage and limited scalability. By introducing a forceful caching framework, which allows the application to serve cached data unless critical updates are made, we can significantly reduce CPU overhead and enhance performance (Smith, 2019, p. 34). This optimization has demonstrated a tenfold increase in throughput and a reduction in CPU usage by 50%, proving its efficacy in streamlining the SF Learning system (Johnson, 2017, p. 57).

Keywords: JVM, Apache Tomcat, Performance, Native OS Resources, Native file read, Cache

Paper Id: 231967

Published On: 2019-01-08

Published In: Volume 7, Issue 1, January-February 2019

Cite This: Optimizing Performance in SAP Success Factors Learning with Efficient Customization Updates and Caching Mechanisms - Pradeep Kumar - IJIRMPS Volume 7, Issue 1, January-February 2019. DOI 10.5281/zenodo.14607728

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