COST06-BP02: Select resource type, size, and number based on data
Use actual usage data, performance metrics, and workload characteristics to make informed decisions about resource type, size, and number rather than relying on assumptions or over-provisioning. Data-driven resource selection ensures optimal cost-performance balance.
Implementation guidance
Data-driven resource selection involves collecting and analyzing actual usage patterns, performance metrics, and workload characteristics to make informed decisions about resource configurations. This approach eliminates guesswork and over-provisioning while ensuring performance requirements are met within cost targets.
Data Collection Strategy
Usage Metrics: Collect comprehensive usage data including CPU, memory, storage, and network utilization across different time periods and load conditions.
Performance Metrics: Monitor application performance metrics such as response time, throughput, and error rates to understand resource requirements.
Business Metrics: Correlate technical metrics with business metrics to understand the relationship between resource usage and business outcomes.
Cost Metrics: Track costs at the resource level to understand the cost implications of different resource configurations.
Analysis Framework
Baseline Analysis: Establish baseline resource requirements based on historical data and performance benchmarks.
Pattern Recognition: Identify usage patterns, peak loads, and seasonal variations to inform resource sizing decisions.
Correlation Analysis: Analyze relationships between different metrics to understand resource dependencies and optimization opportunities.
Predictive Analysis: Use historical data to predict future resource requirements and plan for growth.
AWS Services to Consider
Implementation Steps
1. Establish Data Collection
- Set up comprehensive monitoring and metrics collection
- Configure CloudWatch agents and custom metrics
- Implement application performance monitoring
- Create data pipelines for analysis and storage
2. Define Analysis Framework
- Establish baseline performance and cost metrics
- Create analysis templates and methodologies
- Define decision criteria and thresholds
- Set up automated analysis and reporting
3. Collect Historical Data
- Gather at least 30 days of usage data
- Analyze seasonal patterns and trends
- Identify peak and average usage patterns
- Document workload characteristics and requirements
4. Perform Resource Analysis
- Analyze current resource utilization and efficiency
- Identify over-provisioned and under-provisioned resources
- Correlate resource usage with performance metrics
- Calculate cost per unit of work or transaction
5. Generate Recommendations
- Use data analysis to generate rightsizing recommendations
- Consider different resource types and configurations
- Evaluate trade-offs between cost and performance
- Prioritize recommendations based on impact and effort
6. Implement and Monitor
- Implement resource changes based on data analysis
- Monitor performance and cost impacts
- Validate assumptions and adjust as needed
- Establish ongoing monitoring and optimization processes
Data-Driven Resource Selection Framework
Resource Analytics Engine
Data Analysis Templates
Resource Analysis Report Template
Workload Clustering Analysis
Common Challenges and Solutions
Challenge: Insufficient Historical Data
Solution: Start with available data and gradually extend the analysis period. Use synthetic data generation for testing. Implement comprehensive monitoring from the beginning of new deployments.
Challenge: Seasonal Usage Patterns
Solution: Collect data over multiple seasonal cycles. Use time-series analysis to identify seasonal patterns. Create separate models for different seasons or usage patterns.
Challenge: Complex Multi-Tier Applications
Solution: Analyze application tiers separately and together. Use distributed tracing to understand dependencies. Consider the impact of changes on the entire application stack.
Challenge: Performance vs. Cost Trade-offs
Solution: Define clear performance requirements and SLAs. Use multi-objective optimization techniques. Create cost-performance efficiency metrics to guide decisions.
Challenge: Data Quality and Completeness
Solution: Implement data validation and quality checks. Use multiple data sources for validation. Establish data collection standards and monitoring for data quality.