Platform

How StormForge Optimize Pro Works

StormForge Optimize Pro improves Kubernetes efficiency and provides in-depth application insights through a process of rapid experimentation and scenario analysis using machine learning in your non-production environment.

So… How does it work?

Step 1

In-cluster Resource Scanning

First, StormForge scans your Kubernetes cluster to automatically find all the configurable parameters. This includes Kubernetes resource requests and limits as well as application-specific parameters. We also detect the current values for these parameters to use as a baseline for comparison.

MemoryCPUReplica
Costp95 LatencyThroughputError RateDuration
Step 2

Optimization Objectives

You choose optimization goals based on your business priorities. Goals can include any metric that corresponds to a business objective, for example application response time, throughput or cost. StormForge can optimize for multiple objectives, enabling real business trade-offs between competing goals.

StormForge provides basic cost and latency metrics out of the box, but metrics can also come from your existing monitoring tools, load testing tools, or any other source.

Step 3

Load Testing

Load testing is used to place a realistic production load on the application during the optimization process, which takes place in your test cluster.

Load tests can be performed using StormForge Performance Testing or with a third-party load testing tool.

Step 4

StormForge Machine Learning

The StormForge patent-pending ML algorithm is designed to explore multi-dimensional parameter spaces in a highly efficient way. The model is specific to your app and data, running in your environment.

That means no upfront training of the model is required, and it’s able to recommend an optimal solution much faster than alternative approaches.

Step 5

The Rapid Experimentation Process

With StormForge Optimize Pro, the optimization process is fully automated. It’s managed by the StormForge controller, which runs in your cluster, and consists of several trials.

    Here are the 5 steps involved:

    With each trial, the machine learning engine builds a more complete picture of the multi-dimensional parameter space, homing in on the set of parameters that will deliver the best outcomes, automatically balancing the trade-offs between competing goals.

    Multiple screenshots of the StormForge Kubernetes App Optimization dashboard, depicting multiple parameters and graphs
    Step 6

    Analyzing the Results

    The optimization experiment results are shown on a chart, with each trial represented by a dot plotted against your goals. You can easily see the trials that represent the best trade-offs between competing goals to result in the optimal outcomes. You can also see high-risk configurations that would likely fail in production and should be avoided. Selecting a trial shows the parameters used and the results for that particular trial.

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