Stability and Availability Optimization of Distributed ERP Systems During Cloud Migration

Abstract

Enterprise resource planning is a powerful software package that enables businesses to integrate a variety of disparate functions, it is a concept whereby an organization enables effective communication and data exchange within the business units and associated third parties such as vendors and banks. SAP is the de-facto ERP software for a company that manages business operations and customer relations. Many organizations have started migrating their ERP to infrastructure-as-a-service (IAAS) providers like amazon web services and Microsoft Azure. Though there is a significant increase and positive sentiment to migrate productive workloads to the cloud, the availability and reliability of applications remain a huge concern. No comprehensive study specifically examines the stability and availability of productive ERP workload in the cloud with large databases. Cloud brings specific advantages like faster time-to-value, moving out of expensive hardware, ease of spinning up virtual machines, and accessibility of the latest technologies in one place. There is a need for a study to specifically look into moving productive ERP workload to the cloud and a roadmap to achieve optimum stability and availability while leveraging all the advantages we get from cloud computing. The paper’s objective is to take in the experience of migrating several productive ERP landscapes with a database size ranging from 2 TB to 20 TB and propose a design roadmap to attain optimum Stability and 99.9% Availability. The design was implemented and measured for its effectiveness post-migration by comparing the application with the data before migration. SAP pre-go-live and post-go-live reports show a 33% reduction in dialog response time and a 40% improvement in response time during peak hours. The overall database grew from 8449 GB to 8703 GB during the analysis. When we extrapolate the analysis to 21 days post-migration, we see that the response time improves even further. We see 100% applications available during the analysis period.