The configuration options of Extremem are designed to allow Extremem to mimic the memory workload behavior of broad varieties of real-world applications and services. These workloads are typically characterized in terms of:
- allocation rate trends
- live memory usage trends
- object longevity demographics (e.g. 70% of objects die within 50 ms, additional 20% within 10 s, other 10% within 5 minutes)
If customers have good understandings of their services in terms of memory allocation behaviors, it would be desirable that the documentation of Extremem configuration choices would guide them in selection of the configuration parameters that would cause Extremem to approximately match the memory behavior of their service.
In the absence of this documentation, customers are required to select experimental configuration choices, run Extremem with GC logging enabled, compare the GC log output to the comparable GC log output for their service, and then repeat the experiment with adjustments to the experimental configuration choices, iterating until the observed behavior is sufficiently close to the workload they are trying to model.
The configuration options of Extremem are designed to allow Extremem to mimic the memory workload behavior of broad varieties of real-world applications and services. These workloads are typically characterized in terms of:
If customers have good understandings of their services in terms of memory allocation behaviors, it would be desirable that the documentation of Extremem configuration choices would guide them in selection of the configuration parameters that would cause Extremem to approximately match the memory behavior of their service.
In the absence of this documentation, customers are required to select experimental configuration choices, run Extremem with GC logging enabled, compare the GC log output to the comparable GC log output for their service, and then repeat the experiment with adjustments to the experimental configuration choices, iterating until the observed behavior is sufficiently close to the workload they are trying to model.