Cloud Analysis Policy

Configure and understand Kubex Cloud Analysis Policies for rightsizing, risk detection, scale optimization, and Kubernetes cluster recommendations.

Cloud Analysis Policy

Cloud Analysis Policies define how Kubex evaluates cloud workloads and generates optimization recommendations. Policies control the analysis window, utilization thresholds, scaling constraints, Kubernetes reservation targets, and instance selection preferences.

These settings determine when Kubex recommends:

• Upsizing under-provisioned resources
• Downsizing over-provisioned resources
• Terminating unused instances
• Optimizing scale groups
• Adjusting Kubernetes cluster reservations
• Migrating to more efficient instance families or generations

Figure: Cloud Policy

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Operational Windowing

The operational window determines which historical utilization data is included during analysis.

Setting	Value
Workload Range	60 Days
Include Weekends	Yes
Include Samples Between	0 - 100th Percentile
Exclude Hours	None
Minimum Data for Analysis	1 Day / 1 Hour

Configuration Details

Workload Range

Defines the historical period used for workload analysis.

Current Setting: 60 Days

A longer analysis period captures seasonal trends and workload variability while reducing sensitivity to short-term spikes.

Include Weekends

Determines whether weekend utilization data is included.

Current Setting: Yes

Include Samples Between

Filters utilization samples to eliminate statistical outliers.

Default Setting: 0 - 100th Percentile

Minimum Data Requirement

Metric	Value
Days	1
Hours	1

Kubex requires at least this amount of utilization data before generating recommendations.

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Resource Risk Detection

Resource Risk Detection identifies workloads that may be undersized and at risk of performance degradation.

Recommendations are generated when any configured threshold is exceeded.

CPU Risk Thresholds

Metric	Threshold
Peak	> 95%
Sustained	> 85%
Average	> 75%

Recommendation Logic

Kubex recommends an Upsize when:

Peak CPU > 95%
OR
Sustained CPU > 85%
OR
Average CPU > 75%

Memory Risk Thresholds

Setting	Value
Evaluated Based On	Peak
Threshold	> 95%

Recommendation Logic

Kubex recommends an Upsize when:

Peak Memory Utilization > 95%

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Resource Inefficiency Detection

Resource Inefficiency Detection identifies workloads that are overprovisioned.

Recommendations are generated only when all configured conditions are satisfied.

CPU Inefficiency Thresholds

Metric	Threshold
Peak	< 94%
Sustained	< 84%
Average	< 74%
Maximum Decrease	75%

Recommendation Logic

Kubex recommends a Downsize when:

Peak CPU < 94%
AND
Sustained CPU < 84%
AND
Average CPU < 74%

The recommended decrease will never exceed:

75%

Memory Inefficiency Thresholds

Setting	Value
Evaluated Based On	Sustained
Threshold	< 95%
Maximum Decrease	75%

Recommendation Logic

Kubex evaluates sustained memory utilization and limits reduction recommendations to a maximum 75% decrease.

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Unused Instance Detection

Unused Instance Detection identifies workloads that can potentially be terminated.

Recommendations are generated only when all thresholds are met.

CPU Thresholds

Metric	Threshold
Peak	< 20%
Sustained	< 10%
Average	< 4%

Memory Thresholds

Setting	Value
Evaluated Based On	Peak
Threshold	< 100%

Recommendation Logic

Kubex recommends a Terminate action when:

Peak CPU < 20%
AND
Sustained CPU < 10%
AND
Average CPU < 4%

This policy identifies workloads that demonstrate consistently negligible resource utilization.

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Scale Group Optimization

Scale Group Optimization controls how Kubex recommends adjustments for Auto Scaling Groups and similar scaling constructs.

Configuration

Setting	Value
Group Size	1 - 1000
Allowed Decrease	50%
Allowed Increase	100%

Recommendation Constraints

Kubex will:

• Reduce scale groups by at most 50%
• Increase scale groups by at most 100%
• Analyze groups containing between 1 and 1000 instances

Tying Kubernetes Node Groups to the Scale Groups hosting them

When an AWS ASG or Azure ScaleSet hosts an EKS/AKS cluster, scale group optimization cannot rely on utilization metrics alone. Kubernetes CPU and memory requests must also be included in the analysis.

If Kubex is connected to the Kubernetes clusters, it can correlate scale groups with their node groups and produce actionable recommendations.

CPU Request

Setting	Value
Evaluated Based On	Sustained
Threshold	> 90%

Memory Request

Setting	Value
Evaluated Based On	Sustained
Threshold	> 90%

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Rule Engine

The Rule Engine controls how Kubex selects target instance types during optimization.

Rule	Description
Optimize Family	Allow alternative instance families when a more cost-effective or performant option exists
Generation Changes	Permits migration between instance generations
Favor Latest Generation	Prioritizes newer generation instance types when available
Older Architectures	Allow recommendations to older CPU architectures
Allow Flex Types	Permits recommendations to flexible instance families where supported
Intel ↔ AMD Migration	Allows cross-architecture recommendations between Intel and AMD platforms when cost or performance benefits exist
Allow Storage Optimized	Enables recommendations to storage-optimized instance types when appropriate
Allow Burstable	Allows migration to burstable instance families for suitable workloads
Keep Storage Optimized	Stay on a storage optimized instance
Keep Local Disk	Kubex is allowed to recommend instance types without local storage
Software Affinity	Recommendations are not constrained by software-specific affinity requirements

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Recommendation Summary

With this policy configuration:

• Workloads at risk are upsized when CPU or memory utilization exceeds defined thresholds.
• Overprovisioned workloads are downsized based on CPU and memory efficiency rules.
• Nearly idle workloads are flagged for termination.
• Scale groups can shrink by up to 50% or grow by up to 100%.
• Instance recommendations can migrate across families, generations, and CPU vendors to maximize efficiency and cost savings.

This policy balances performance protection, cost optimization, and infrastructure modernization while maintaining operational flexibility.