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App specific infrastructure optimization

Improves performance and saves time & money

Better Performance

Bigger is not always better. Selecting an optimal compute instance size that accounts for the app's resource utilization behavior and load characteristics can improve performance. When the probable capacity of compute instance types are known, they can be selected based on deployment strategy and cost.

Adaptive to Changing Load Characteristics

Resource utilization is dependent on load characteristics, which are a mix of different workloads with each class requiring different resources. Load characteristics change over time and can be cyclical. Matching the optimal instance types to changing load characteristics results in better performance.

Smaller Deployment Footprint

Load characteristics change over time and may also be cyclical. Since cloud deployments can be elastic, rather than the cluster always using the same compute instance type, it can instead be configured to use fewer compute instances by using instance types that are optimal to specific load characteristics.


Understanding app resource behavior enables Operations to manage deployment and costs better. When you know the maximum capacity of different compute instance types it is easier to deploy strategies focused on minimizing footprint.

What Machine Learning does for you

Workload Change Detection

Machine learning algorithms classify load mixes and detect changes in load characteristics. Understanding the load characteristics makes it easier to determine the optimal compute for when an app receives the load.

Optimal Instance Type Selection

Machine learning algorithms understand the resource utilization and behavior of the app. They use ranking and forecasting techniques to select instance type and forecast the probable capacity within required SLO threshold.

Tune to Changing Load Characteristics of the App

Apps Receive Changing Loads

Performance tuning needs to be specific to the load characteristics that the app is undergoing. But load characteristics are dynamic and periodic.

Automatic Detection of Load Profile Changes

AskLytics Machine Learning detects when load profiles change and when to apply the appropriate JVM settings.

Automatic Classification of Workloads

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.

Compute Instances That Match Your App’s Workload

Because different instances are better for different workloads

Work Profile Specific Instance

Recommendations based on the profile of the load and the workload classification.

Instance Type from Available Choices

Recommendations based on the compute instance choices available from the IaaS vendor.

Select the optimal compute with ease!

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