Best Practice 16.5 – Scale to meet
performance demands
One of the primary benefits of operating workloads in AWS is the ability to increase or decrease the compute capacity and change the storage performance characteristics to match the performance required for the use case. For SAP workloads, use dynamic scaling where applicable to avoid performance bottlenecks. Scenarios where dynamic scaling is not possible, such as scaling out an SAP HANA database cluster, use a manual deployment process.
Suggestion 16.5.1 – Reactively scale SAP workloads
In response to dynamic changes in workload performance requirements, scale your SAP resources accordingly. Where possible, use automation to scale in or out, but when that is not an option (such as scaling up a database instance), have a process in place to do so manually. Consider:
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Adding or removing application server capacity or changing instance sizes as required to meet demand
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Changing SAP parameters to redistribute virtual resources programmatically
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Modifying storage type (for example, Amazon EBS
gp3toio2or vice versa in AWS), where applicable, to optimize storage performance
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AWS Documentation: Request modifications to your EBS volumes
Suggestion 16.5.2 – Schedule scaling for predictable SAP workloads
Whether in an automated or manual fashion, scaling an SAP workload up or down based on predictable performance patterns is advisable. For instance, when month-end financial processing on an SAP ECC system leads to a predictable 20% increase in processing requirements on application server instances, system administrators can proactively increase the number or size of application servers, then scale-in the number of instances when the usage predictably decreases.
