Cloud Resource Rightsizing Agent
Analyzes actual compute, memory, and storage consumption against provisioned capacity to recommend precise instance and volume rightsizing across cloud environments.
Infrastructure is commonly provisioned for anticipated peak load and then never revisited as actual traffic patterns stabilize, leaving compute instances, containers, and storage volumes running well above what workloads actually require
Engineers default to oversizing during initial deployment to avoid performance risk, and without ongoing analysis of real utilization data, that initial oversizing becomes permanent
Rightsizing analysis performed manually requires pulling utilization data across many services and time windows, a task tedious enough that it's usually only done during an annual infrastructure review if at all
This agent continuously analyzes real utilization patterns across compute, memory, storage, and container resources, and generates precise, workload-aware rightsizing recommendations with performance risk assessments attached
The agent pulls granular utilization metrics for CPU, memory, disk I/O, and network across compute instances, containers, and storage volumes over rolling time windows that capture both average and peak load patterns. It models the minimum resource allocation that would have safely handled observed peak demand plus a configurable safety margin, then compares this against current provisioning to generate specific rightsizing recommendations with projected cost savings and performance risk scoring. Recommendations are grouped by confidence level, with high-confidence, low-risk changes eligible for auto-approval workflows.
Collect Granular Utilization Data
- Pull CPU, memory, disk, and network metrics across compute and storage resources
- Capture both average and peak utilization over rolling windows
- Segment data by workload type and traffic pattern
- Identify resources with insufficient history for confident sizing
Model Optimal Sizing
- Calculate minimum resource allocation to safely cover observed peak demand
- Apply configurable safety margins by workload criticality
- Compare modeled optimal size against current provisioning
- Flag resources with insufficient data as needing longer observation
Generate Rightsizing Recommendations
- Produce specific instance type or volume size recommendations
- Estimate cost savings and performance risk for each recommendation
- Group recommendations by confidence level for approval routing
- Route high-confidence, low-risk changes for auto-approval
Execute and Validate
- Apply approved rightsizing changes through infrastructure-as-code
- Monitor post-change performance to confirm no degradation
- Auto-rollback if performance regresses beyond threshold
- Track cumulative savings realized from rightsizing actions