Performance Challenges on the Cloud

 

 

A typical cloud implementation could have hundreds of CPUs running in parallel. Some cloud solutions could be like an S-class Mercedes, however to leverage its muscles, it needs to be configured and fine tuned to reflect an efficient model. Examples of factors affecting a cloud implementation performance would be the number of Key Figures in an Integrated Business Planning (IBP) solution. The more Key Figures the higher the demand on the system and lower the performance.

 

Examples of common cloud performance challenges I have seen on SAP and Salesforce implementations are:

• Slow login time which is usually driven by network latency, demand on the enterprise SSO or other security system, firewall settings, or network device configurations.
• Dropped connections could be difficult to troubleshoot. On one implementation SAP users would receive a dropped connection error on an intermittent basis after about 30 minutes of being active on the system. Two weeks of troubleshooting uncovered the root cause of the issue was not due to the SAP cloud, but a misconfigured load balancer group of servers at one of the client's data centers.
• Initial load of templates, views and report runs are common issues when customers process large amounts of data. On a healthcare SFDC implementation the customer had a Contacts view for all its members, a whopping 1.5 million records. Obviously loading the view would take a few minutes of its agents staring at their screen. Educating the client on customizing the views to show members in a particular region, or who match certain filter criteria allowed them to view smaller data sets which were also more meaningful to their work.
• Refresh time is also a factor of the amount of data being retrieved, the browser
• Simulations
• Long running queries

 

Vendors constantly monitor their cloud operations checking memory utilization, background jobs status, CPU utilization, table sizes, system activities, and other performance KPIs to ensure optimum performance for their customers.

SAP key figure calculations can reach 10 million rows per second for its IBP solution in some implementations. Some factors that can affect that metric are data aggregations, projection, joins and uinions. In general loading a Planning View is dependent on the slowest Key Figure atomic calculation speed and the volume of data it processes. Using filters can sometimes be effective if it reduces the number of stored Key Figures that are defined as inputs to a calculation chain. However if filters do not affect the number of key figures and only affects data size the impact on performance might be minimal.