The Kubernetes 

Kalc is AI-first application aware cluster rebalancing for Kubernetes.
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How does it work ?

Kalc generates exact kubectl commands and YAML diffs by reasoning against current cluster state and intelligently resolves conflicts and potential issues with your intended changes.

Step 1: Incrementally define your policies

Use a selection of use-case oriented policies to apply to your cluster and mix them with intents like rebooting with minimal impact or removing excess nodes. 

Step 2: Compute the changes

Run kalc to get exact command sequence that is required to satisfy your current policy and all the previously introduced policies.

Step 3: Apply changes 

Kalc generates YAML diffs in json-patch format that can be applied both in-place to running cluster and to your manifests stored in source control. 

Step 4: Create your own high-level policies

You can define your own service level objectives, dependencies and constraints by manipulating state objects using pure Python language, and use the same efficient engine to compute optimal satisfiability scenarios.

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Calculate Correct Resource Request and Limits

Automatically Inject Anti Affinity Rules

Estimate cost of your next feature or deployment scaling

Find unused nodes that are safe to switch off

Translate intent to config in seconds

Eliminate most of manual guesswork in your day-to-day kubernetes tasks with machine logic engine implemented in kalc.
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