Introduction 

In this blog post, we’ll delve into application: ILM Advisor available as part of SAP S/4HANA Cloud solution that offers capabilities to monitor, analyse the memory consumption by applications and the related SAP Information Lifecycle Management objects. 

Note: This blog was created in a team effort with our colleagues Claudia Semmler and Sudhanva Laxminarayan

Motivation 

Imagine having an application that helps not only monitor, analyse by applications and the related SAP Information Lifecycle Management objects but also provides transparency on the potential for data reduction of your biggest data consumers by ILM objects. 

This application is designed with simulation capabilities to compute data reduction potential using residence time and helps you make an educated decision on which ILM service objects to focus on when configure and perform archiving for the identified ILM objects. 

Benefits of the Application 

• Guidance to Reduce Data Footprint 

• • Helps users in identifying and focusing on the most relevant ILM objects for memory reduction. 
  • By moving historic data from memory to archive files using ILM objects, the application helps to effectively reduce the data footprint. 

Key Features of the Application 

• Memory Consumption Analysis
  • Analyses and monitors memory consumption by applications and fast growing tables mapped to relevant ILM objects. 

• Simulate and Compute Data Reduction Potential
  • Provides transparency on data reduction potential for significant data consumers related to ILM objects. 

• History of Memory Consumption
  • Provides a comprehensive history and details of the memory consumed for analysed ILM objects. 

How to use 

First step – get an overview on memory consumption 

You want to know how much memory of your current subscription is allocated by your business data (as is situation). Furthermore, you may be interested how data volumes developed over the past. The historic view will help you to e.g. match roll-outs to additional plants or sites to match with a growth in volumes and also to extrapolate historic growth to the future so that you get an idea when you may need to add additional memory to your subscription. 

Section: ‘Total Memory Usage’ this information will be provided incl. historical data spanning back 18 months and offering insights into memory usage trends. 

This allows you to 

• Detect unexpected growth e.g. maybe a new process was set live that changed the system behaviour in an unexpected way. There is sometimes the case that master data integration processes cause an unwanted high number of change documents as the process runs too often or in a too detailed manner. 
• Plan for a maybe required extension of your subscription ahead of time 
• Plan for ILM measures like data archiving to reduce growth and memory consumption by having transparency and guidance on which business objects to focus on. 

Second step – get into details of main contributors to memory consumption 

After getting an overview that helps you to decide whether actions are required or not, you can drill-down to some more detail and get a breakdown of memory consumption by the main contributing business data and their corresponding ILM objects. (Section: ‘Memory Usage Statistics’ 

But in addition to the current size of the business data there are two additional factors that decide about whether you want to take actions or not 

• Age of data
  • Sometimes the data is still too fresh to be archived; an average best practice for residence time, i.e. the time when data is moved from the database to an archive file is about 2 years … this depends in detail on the business object. So, there may be cases when the expected reduction of memory you could achieve by data archiving is very less, as the data is still required by the business end-users and needs to remain integrated in the business processes.
  • In order to give an estimation about possible gains by data archiving the ILM Advisor offers a simulation that considers the age of the data combined with the required residence time. You can define how long the data is required to stay on the database and the ILM Advisor computes which data is older that timeframe and there can be archived and can be considered as reduction potential. So, you can play around, change the residence time and see the effect on the reduction potential to find a good balance.
    Note: Simulating residence times in this app does not affect actual ILM framework archiving job execution or configurations in the ILM Policies app. 
• Business relevance
  • The residence time which has a strong influence on the reduction potential depends on the need of the business users (and auditors) to access and process the data. There are simple and complex data, e.g. archiving application logs is usually easier to accept by business users as after a few months the data gets less relevant compared to accounting or controlling data, which may be required long and in full detail. 

Third step – double check the relevance and focus against data growth 

In section ‘Memory Usage Statistics’ you can see the as-is-situation of size and reduction potential. So you would naturally look for those ILM objects that offer the most significant data reduction potential. But with this view only, you may miss recent developments, i.e. due to a change in business processes there is new data starting to growth that doesn’t have reached the size of the other business objects, but may become a concern. 

To identify ‘newcomers’ and determine shifts in growth rates section ‘Growth statistics’ shows the fastest growing objects. The application features a list ranking ILM service objects based on memory changes in the preceding three months. Metrics such as memory change in units and percentages, along with total memory as of the current date, provide valuable insights. The dashboard offers filtering options based on different time frames, allowing a detailed analysis of memory growth trends. 

So, in case there is something unexpected or new, that may be discussed with the process owners to check if this is works-as-designed you will notice it by checking the data growth history. 

Note:  

ILM Advisor considers the tables which are fast growing, and the memory consumption metrics are restricted to these fast-growing tables.  

Fast growing tables were identified based on analysis performed in customer S/4HANA Cloud public edition tenants. If there is a major difference in covering the memory usage statistics i.e., if ILM Advisor is covering only 30% or 1/3rd of memory used in your respective SAP S/4HANA Cloud public edition tenant please submit a new request to consider and add the missing tables via customer influence portal. 

Conclusion 

ILM Advisor application will be useful and beneficial to monitor, analyse memory consumption and provides guidance on achieving memory reduction by identifying the most relevant ILM objects. By utilising this app, businesses can enhance their memory usage strategies and streamline their operations.