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Each app behaves differently based on its workload and system resource requirements. Tuning needs to account for that.
Not all loads are alike. Over time, apps are subjected to different load characteristics. Tuning needs to account for these differences. PaaS deployments can instantiate JVM with settings that are tuned for specific time periods.
AskLytics algorithms detect changes in the load profiles and account for the app’s behavior and resource consumptions when recommending optimal settings.
When apps are receiving requests that are shorting running such as web pages, optimizing the JVM Settings for low latencies will make your apps thrive.
TWhen apps are receiving requests that take time, such as data analysis, optimizing the JVM Setting for high throughput will make your apps be the best workhorse they can be.
Tuning needs to be specific to the load characteristics that the app is undergoing. But load characteristics are dynamic and periodic.
AskLytics Machine Learning detects when load profiles change and when to apply the appropriate JVM settings.
Apps receive different classes of work. Some could be short running while others are long-running. The system resources consumed are based on the mix of workloads that affect the JVM settings. AskLytics Machine Learning determines the mix and classification of every workload.
Machine learning at your service
Recommendations are made based on the profile of the load that the app is under. Since loads are often cyclical, in dynamic PaaS deployments you can use different JVM settings based on time.
Recommendations are based on the mix of the workload that the app is under. When there are long running workloads, optimize the throughput instead latency.
Optimal JVM Settings
Better Infrastructure Performance!
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