COST09
COST09-BP01 - Perform an analysis on the workload demand
Implementation guidance
Workload demand analysis involves systematically collecting, analyzing, and interpreting usage data to understand how resources are consumed over time. This analysis helps identify patterns, predict future needs, and optimize resource allocation to minimize costs while maintaining performance and availability requirements.
Demand Analysis Dimensions
Temporal Patterns: Analyze demand variations over different time periods including hourly, daily, weekly, monthly, and seasonal patterns to understand cyclical usage.
Usage Characteristics: Examine workload characteristics including transaction volumes, user activity, data processing requirements, and resource consumption patterns.
Growth Trends: Identify growth patterns and trends to predict future capacity requirements and plan for scaling needs.
Variability Analysis: Understand demand variability and volatility to design appropriate buffering and scaling strategies.
Business Correlation: Correlate technical demand patterns with business events, marketing campaigns, and external factors that influence usage.
Analysis Categories
Historical Analysis: Examine past usage data to identify patterns, trends, and anomalies that inform future resource planning.
Real-Time Analysis: Monitor current demand patterns and resource utilization to understand immediate optimization opportunities.
Predictive Analysis: Use statistical models and machine learning to forecast future demand and capacity requirements.
Comparative Analysis: Compare demand patterns across different workloads, environments, and time periods to identify optimization opportunities.
AWS Services to Consider
Amazon CloudWatch
Collect and analyze metrics on resource utilization, application performance, and demand patterns. Use CloudWatch for comprehensive demand monitoring and analysis.
AWS Cost Explorer
Analyze cost patterns and correlate them with usage trends. Use Cost Explorer to understand the financial impact of demand patterns and identify optimization opportunities.
Amazon QuickSight
Create advanced analytics dashboards and visualizations for demand analysis. Use QuickSight to identify patterns and trends in large datasets.
AWS X-Ray
Analyze application performance and identify demand patterns at the service level. Use X-Ray to understand how demand flows through your application architecture.
Amazon Kinesis
Stream and analyze real-time demand data for immediate insights. Use Kinesis for real-time demand pattern analysis and anomaly detection.
AWS Glue
Process and transform demand data from multiple sources for comprehensive analysis. Use Glue to create unified demand datasets for analysis.
Implementation Steps
1. Define Analysis Objectives
- Establish clear goals for demand analysis and optimization
- Define key metrics and success criteria for analysis
- Identify stakeholders and reporting requirements
- Set up data collection and analysis infrastructure
2. Collect Demand Data
- Implement comprehensive monitoring across all workload components
- Collect usage metrics, performance data, and business metrics
- Set up data pipelines for automated data collection and processing
- Ensure data quality and completeness for accurate analysis
3. Analyze Historical Patterns
- Examine historical usage data to identify patterns and trends
- Analyze seasonal variations and cyclical patterns
- Identify growth trends and capacity planning requirements
- Document findings and create baseline demand profiles
4. Implement Real-Time Analysis
- Set up real-time monitoring and analysis capabilities
- Create dashboards for immediate demand visibility
- Implement alerting for demand anomalies and threshold breaches
- Enable real-time decision making based on current demand
5. Develop Predictive Models
- Create statistical models for demand forecasting
- Implement machine learning algorithms for pattern recognition
- Validate model accuracy and refine predictions
- Use predictions for proactive resource planning
6. Create Analysis Framework
- Establish regular analysis cycles and reporting schedules
- Create standardized analysis templates and methodologies
- Implement automated analysis and reporting where possible
- Share insights and recommendations with stakeholders
Workload Demand Analysis Framework
Demand Pattern Analyzer
View code
import boto3
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import json
from scipy import stats
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
@dataclass
class DemandMetric:
timestamp: datetime
metric_name: str
value: float
unit: str
resource_id: str
workload_name: str
@dataclass
class DemandPattern:
pattern_type: str # hourly, daily, weekly, seasonal
pattern_strength: float # 0-1 correlation strength
peak_periods: List[str]
low_periods: List[str]
variability_coefficient: float
trend_direction: str # increasing, decreasing, stable
@dataclass
class DemandForecast:
forecast_date: datetime
predicted_value: float
confidence_interval_lower: float
confidence_interval_upper: float
forecast_accuracy: float
class WorkloadDemandAnalyzer:
def __init__(self):
self.cloudwatch = boto3.client('cloudwatch')
self.ce_client = boto3.client('ce')
self.quicksight = boto3.client('quicksight')
# Analysis parameters
self.analysis_window_days = 90
self.forecast_horizon_days = 30
self.pattern_detection_threshold = 0.7
def collect_demand_metrics(self, workload_config: Dict,
start_date: datetime, end_date: datetime) -> List[DemandMetric]:
"""Collect comprehensive demand metrics for workload analysis"""
metrics = []
# Define key demand metrics to collect
demand_metrics_config = [
{'namespace': 'AWS/EC2', 'metric': 'CPUUtilization', 'stat': 'Average'},
{'namespace': 'AWS/ApplicationELB', 'metric': 'RequestCount', 'stat': 'Sum'},
{'namespace': 'AWS/ApplicationELB', 'metric': 'TargetResponseTime', 'stat': 'Average'},
{'namespace': 'AWS/Lambda', 'metric': 'Invocations', 'stat': 'Sum'},
{'namespace': 'AWS/Lambda', 'metric': 'Duration', 'stat': 'Average'},
{'namespace': 'AWS/DynamoDB', 'metric': 'ConsumedReadCapacityUnits', 'stat': 'Sum'},
{'namespace': 'AWS/DynamoDB', 'metric': 'ConsumedWriteCapacityUnits', 'stat': 'Sum'},
{'namespace': 'AWS/RDS', 'metric': 'DatabaseConnections', 'stat': 'Average'},
{'namespace': 'AWS/RDS', 'metric': 'CPUUtilization', 'stat': 'Average'}
]
for resource_id in workload_config.get('resource_ids', []):
for metric_config in demand_metrics_config:
try:
# Get metric data from CloudWatch
response = self.cloudwatch.get_metric_statistics(
Namespace=metric_config['namespace'],
MetricName=metric_config['metric'],
Dimensions=[
{'Name': self.get_dimension_name(metric_config['namespace']), 'Value': resource_id}
],
StartTime=start_date,
EndTime=end_date,
Period=3600, # 1 hour periods
Statistics=[metric_config['stat']]
)
# Convert to DemandMetric objects
for datapoint in response['Datapoints']:
metric = DemandMetric(
timestamp=datapoint['Timestamp'],
metric_name=f"{metric_config['namespace']}.{metric_config['metric']}",
value=datapoint[metric_config['stat']],
unit=datapoint['Unit'],
resource_id=resource_id,
workload_name=workload_config['workload_name']
)
metrics.append(metric)
except Exception as e:
print(f"Error collecting metric {metric_config['metric']} for {resource_id}: {e}")
continue
return metrics
def analyze_demand_patterns(self, metrics: List[DemandMetric]) -> Dict[str, DemandPattern]:
"""Analyze demand patterns from collected metrics"""
if not metrics:
return {}
# Convert to DataFrame for analysis
df = pd.DataFrame([
{
'timestamp': m.timestamp,
'metric_name': m.metric_name,
'value': m.value,
'resource_id': m.resource_id,
'workload_name': m.workload_name,
'hour': m.timestamp.hour,
'day_of_week': m.timestamp.weekday(),
'day_of_month': m.timestamp.day,
'month': m.timestamp.month
}
for m in metrics
])
patterns = {}
# Analyze patterns for each metric type
for metric_name in df['metric_name'].unique():
metric_data = df[df['metric_name'] == metric_name].copy()
if len(metric_data) < 24: # Need at least 24 hours of data
continue
# Hourly patterns
hourly_pattern = self.detect_hourly_pattern(metric_data)
if hourly_pattern:
patterns[f"{metric_name}_hourly"] = hourly_pattern
# Daily patterns
daily_pattern = self.detect_daily_pattern(metric_data)
if daily_pattern:
patterns[f"{metric_name}_daily"] = daily_pattern
# Weekly patterns
if len(metric_data) >= 168: # At least one week of data
weekly_pattern = self.detect_weekly_pattern(metric_data)
if weekly_pattern:
patterns[f"{metric_name}_weekly"] = weekly_pattern
# Monthly/seasonal patterns
if len(metric_data) >= 720: # At least one month of data
seasonal_pattern = self.detect_seasonal_pattern(metric_data)
if seasonal_pattern:
patterns[f"{metric_name}_seasonal"] = seasonal_pattern
return patterns
def detect_hourly_pattern(self, metric_data: pd.DataFrame) -> Optional[DemandPattern]:
"""Detect hourly demand patterns"""
# Group by hour and calculate average
hourly_avg = metric_data.groupby('hour')['value'].mean()
# Calculate pattern strength using coefficient of variation
pattern_strength = hourly_avg.std() / hourly_avg.mean() if hourly_avg.mean() > 0 else 0
if pattern_strength > self.pattern_detection_threshold:
# Identify peak and low periods
peak_hours = hourly_avg.nlargest(3).index.tolist()
low_hours = hourly_avg.nsmallest(3).index.tolist()
return DemandPattern(
pattern_type='hourly',
pattern_strength=min(pattern_strength, 1.0),
peak_periods=[f"{hour:02d}:00" for hour in peak_hours],
low_periods=[f"{hour:02d}:00" for hour in low_hours],
variability_coefficient=pattern_strength,
trend_direction=self.calculate_trend(hourly_avg.values)
)
return None
def detect_daily_pattern(self, metric_data: pd.DataFrame) -> Optional[DemandPattern]:
"""Detect daily demand patterns"""
# Group by day of week and calculate average
daily_avg = metric_data.groupby('day_of_week')['value'].mean()
# Calculate pattern strength
pattern_strength = daily_avg.std() / daily_avg.mean() if daily_avg.mean() > 0 else 0
if pattern_strength > self.pattern_detection_threshold:
# Map day numbers to names
day_names = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
peak_days = [day_names[day] for day in daily_avg.nlargest(2).index.tolist()]
low_days = [day_names[day] for day in daily_avg.nsmallest(2).index.tolist()]
return DemandPattern(
pattern_type='daily',
pattern_strength=min(pattern_strength, 1.0),
peak_periods=peak_days,
low_periods=low_days,
variability_coefficient=pattern_strength,
trend_direction=self.calculate_trend(daily_avg.values)
)
return None
def detect_weekly_pattern(self, metric_data: pd.DataFrame) -> Optional[DemandPattern]:
"""Detect weekly demand patterns"""
# Create week number and group by week
metric_data['week'] = metric_data['timestamp'].dt.isocalendar().week
weekly_avg = metric_data.groupby('week')['value'].mean()
if len(weekly_avg) < 4: # Need at least 4 weeks
return None
# Calculate pattern strength
pattern_strength = weekly_avg.std() / weekly_avg.mean() if weekly_avg.mean() > 0 else 0
if pattern_strength > self.pattern_detection_threshold:
peak_weeks = weekly_avg.nlargest(2).index.tolist()
low_weeks = weekly_avg.nsmallest(2).index.tolist()
return DemandPattern(
pattern_type='weekly',
pattern_strength=min(pattern_strength, 1.0),
peak_periods=[f"Week {week}" for week in peak_weeks],
low_periods=[f"Week {week}" for week in low_weeks],
variability_coefficient=pattern_strength,
trend_direction=self.calculate_trend(weekly_avg.values)
)
return None
def detect_seasonal_pattern(self, metric_data: pd.DataFrame) -> Optional[DemandPattern]:
"""Detect seasonal demand patterns"""
# Group by month and calculate average
monthly_avg = metric_data.groupby('month')['value'].mean()
if len(monthly_avg) < 3: # Need at least 3 months
return None
# Calculate pattern strength
pattern_strength = monthly_avg.std() / monthly_avg.mean() if monthly_avg.mean() > 0 else 0
if pattern_strength > self.pattern_detection_threshold:
month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
peak_months = [month_names[month-1] for month in monthly_avg.nlargest(2).index.tolist()]
low_months = [month_names[month-1] for month in monthly_avg.nsmallest(2).index.tolist()]
return DemandPattern(
pattern_type='seasonal',
pattern_strength=min(pattern_strength, 1.0),
peak_periods=peak_months,
low_periods=low_months,
variability_coefficient=pattern_strength,
trend_direction=self.calculate_trend(monthly_avg.values)
)
return None
def calculate_trend(self, values: np.ndarray) -> str:
"""Calculate trend direction for a series of values"""
if len(values) < 3:
return 'stable'
# Calculate linear regression slope
x = np.arange(len(values))
slope, _, r_value, _, _ = stats.linregress(x, values)
# Determine trend based on slope and correlation
if abs(r_value) < 0.3: # Weak correlation
return 'stable'
elif slope > 0:
return 'increasing'
else:
return 'decreasing'
def create_demand_forecast(self, metrics: List[DemandMetric],
forecast_horizon_days: int = 30) -> List[DemandForecast]:
"""Create demand forecasts based on historical patterns"""
forecasts = []
if not metrics:
return forecasts
# Convert to DataFrame
df = pd.DataFrame([
{
'timestamp': m.timestamp,
'metric_name': m.metric_name,
'value': m.value
}
for m in metrics
])
# Create forecasts for each metric type
for metric_name in df['metric_name'].unique():
metric_data = df[df['metric_name'] == metric_name].copy()
metric_data = metric_data.sort_values('timestamp')
if len(metric_data) < 24: # Need sufficient historical data
continue
# Simple moving average forecast with trend adjustment
window_size = min(24, len(metric_data) // 4) # Use 1/4 of data or 24 hours max
recent_values = metric_data['value'].tail(window_size)
forecast_base = recent_values.mean()
# Calculate trend
if len(recent_values) > 1:
trend = (recent_values.iloc[-1] - recent_values.iloc[0]) / len(recent_values)
else:
trend = 0
# Generate forecasts
last_timestamp = metric_data['timestamp'].max()
for days_ahead in range(1, forecast_horizon_days + 1):
forecast_timestamp = last_timestamp + timedelta(days=days_ahead)
# Apply trend and seasonal adjustments
forecast_value = forecast_base + (trend * days_ahead)
# Calculate confidence interval (simplified)
std_dev = recent_values.std()
confidence_interval = 1.96 * std_dev # 95% confidence interval
forecast = DemandForecast(
forecast_date=forecast_timestamp,
predicted_value=max(0, forecast_value), # Ensure non-negative
confidence_interval_lower=max(0, forecast_value - confidence_interval),
confidence_interval_upper=forecast_value + confidence_interval,
forecast_accuracy=0.8 # Simplified accuracy estimate
)
forecasts.append(forecast)
return forecasts
def analyze_demand_variability(self, metrics: List[DemandMetric]) -> Dict:
"""Analyze demand variability and volatility"""
if not metrics:
return {}
# Convert to DataFrame
df = pd.DataFrame([
{
'timestamp': m.timestamp,
'metric_name': m.metric_name,
'value': m.value,
'workload_name': m.workload_name
}
for m in metrics
])
variability_analysis = {}
for metric_name in df['metric_name'].unique():
metric_data = df[df['metric_name'] == metric_name]['value']
if len(metric_data) < 10:
continue
analysis = {
'mean': metric_data.mean(),
'std_dev': metric_data.std(),
'coefficient_of_variation': metric_data.std() / metric_data.mean() if metric_data.mean() > 0 else 0,
'min_value': metric_data.min(),
'max_value': metric_data.max(),
'percentiles': {
'p25': metric_data.quantile(0.25),
'p50': metric_data.quantile(0.50),
'p75': metric_data.quantile(0.75),
'p90': metric_data.quantile(0.90),
'p95': metric_data.quantile(0.95),
'p99': metric_data.quantile(0.99)
},
'volatility_category': self.categorize_volatility(metric_data.std() / metric_data.mean() if metric_data.mean() > 0 else 0)
}
variability_analysis[metric_name] = analysis
return variability_analysis
def categorize_volatility(self, coefficient_of_variation: float) -> str:
"""Categorize demand volatility based on coefficient of variation"""
if coefficient_of_variation < 0.2:
return 'low'
elif coefficient_of_variation < 0.5:
return 'medium'
elif coefficient_of_variation < 1.0:
return 'high'
else:
return 'very_high'
def create_demand_analysis_report(self, workload_name: str, metrics: List[DemandMetric],
patterns: Dict[str, DemandPattern],
variability: Dict, forecasts: List[DemandForecast]) -> Dict:
"""Create comprehensive demand analysis report"""
report = {
'workload_name': workload_name,
'analysis_date': datetime.now().isoformat(),
'analysis_period': {
'start_date': min(m.timestamp for m in metrics).isoformat() if metrics else None,
'end_date': max(m.timestamp for m in metrics).isoformat() if metrics else None,
'total_data_points': len(metrics)
},
'demand_patterns': {
'identified_patterns': len(patterns),
'pattern_details': {
pattern_id: {
'type': pattern.pattern_type,
'strength': pattern.pattern_strength,
'peak_periods': pattern.peak_periods,
'low_periods': pattern.low_periods,
'trend': pattern.trend_direction
}
for pattern_id, pattern in patterns.items()
}
},
'variability_analysis': variability,
'demand_forecasts': {
'forecast_horizon_days': self.forecast_horizon_days,
'total_forecasts': len(forecasts),
'forecast_summary': self.summarize_forecasts(forecasts)
},
'optimization_recommendations': self.generate_optimization_recommendations(patterns, variability),
'capacity_planning_insights': self.generate_capacity_insights(patterns, variability, forecasts)
}
return report
def generate_optimization_recommendations(self, patterns: Dict[str, DemandPattern],
variability: Dict) -> List[Dict]:
"""Generate optimization recommendations based on demand analysis"""
recommendations = []
# Analyze patterns for optimization opportunities
for pattern_id, pattern in patterns.items():
if pattern.pattern_strength > 0.8: # Strong pattern
if pattern.pattern_type == 'hourly':
recommendations.append({
'type': 'scheduled_scaling',
'description': f'Strong hourly pattern detected - implement scheduled scaling',
'pattern': pattern_id,
'peak_periods': pattern.peak_periods,
'low_periods': pattern.low_periods,
'potential_savings': '20-40%',
'implementation_effort': 'Medium'
})
elif pattern.pattern_type == 'daily':
recommendations.append({
'type': 'weekly_scaling',
'description': f'Strong daily pattern detected - optimize for weekday/weekend differences',
'pattern': pattern_id,
'peak_periods': pattern.peak_periods,
'low_periods': pattern.low_periods,
'potential_savings': '15-30%',
'implementation_effort': 'Medium'
})
# Analyze variability for recommendations
for metric_name, var_analysis in variability.items():
volatility = var_analysis['volatility_category']
if volatility == 'high' or volatility == 'very_high':
recommendations.append({
'type': 'demand_buffering',
'description': f'High variability in {metric_name} - implement buffering/queuing',
'metric': metric_name,
'volatility': volatility,
'coefficient_of_variation': var_analysis['coefficient_of_variation'],
'potential_savings': '10-25%',
'implementation_effort': 'High'
})
elif volatility == 'low':
recommendations.append({
'type': 'reserved_capacity',
'description': f'Low variability in {metric_name} - consider reserved capacity',
'metric': metric_name,
'volatility': volatility,
'potential_savings': '30-50%',
'implementation_effort': 'Low'
})
return recommendations
def get_dimension_name(self, namespace: str) -> str:
"""Get appropriate dimension name for CloudWatch namespace"""
dimension_mapping = {
'AWS/EC2': 'InstanceId',
'AWS/ApplicationELB': 'LoadBalancer',
'AWS/Lambda': 'FunctionName',
'AWS/DynamoDB': 'TableName',
'AWS/RDS': 'DBInstanceIdentifier'
}
return dimension_mapping.get(namespace, 'ResourceId')Demand Analysis Templates
Workload Demand Analysis Template
View code
Workload_Demand_Analysis:
analysis_id: "WDA-2024-001"
workload_name: "e-commerce-platform"
analysis_date: "2024-01-15"
analysis_period:
start_date: "2023-10-15"
end_date: "2024-01-15"
duration_days: 92
demand_metrics_analyzed:
- metric: "AWS/ApplicationELB.RequestCount"
resource_count: 3
data_points: 6624
- metric: "AWS/EC2.CPUUtilization"
resource_count: 12
data_points: 26496
- metric: "AWS/DynamoDB.ConsumedReadCapacityUnits"
resource_count: 5
data_points: 11040
identified_patterns:
hourly_request_pattern:
pattern_type: "hourly"
pattern_strength: 0.85
peak_periods: ["10:00", "14:00", "20:00"]
low_periods: ["02:00", "05:00", "07:00"]
trend_direction: "stable"
daily_usage_pattern:
pattern_type: "daily"
pattern_strength: 0.72
peak_periods: ["Tuesday", "Wednesday", "Thursday"]
low_periods: ["Saturday", "Sunday"]
trend_direction: "increasing"
seasonal_pattern:
pattern_type: "seasonal"
pattern_strength: 0.68
peak_periods: ["Nov", "Dec"]
low_periods: ["Jan", "Feb"]
trend_direction: "stable"
variability_analysis:
request_count:
mean: 1250.5
std_dev: 425.3
coefficient_of_variation: 0.34
volatility_category: "medium"
percentiles:
p50: 1180.0
p90: 1850.0
p95: 2100.0
p99: 2650.0
cpu_utilization:
mean: 45.2
std_dev: 18.7
coefficient_of_variation: 0.41
volatility_category: "medium"
percentiles:
p50: 42.0
p90: 68.0
p95: 75.0
p99: 85.0
demand_forecasts:
forecast_horizon_days: 30
forecast_accuracy: 0.82
predicted_growth_rate: 0.15 # 15% monthly growth
peak_demand_forecast:
date: "2024-02-14"
predicted_requests_per_hour: 2850
confidence_interval: [2400, 3300]
optimization_recommendations:
- type: "scheduled_scaling"
priority: "high"
description: "Implement hourly scheduled scaling based on strong pattern"
potential_savings: "25-35%"
implementation_effort: "medium"
- type: "weekend_optimization"
priority: "medium"
description: "Reduce capacity during low-demand weekends"
potential_savings: "15-20%"
implementation_effort: "low"
- type: "demand_buffering"
priority: "medium"
description: "Implement queuing for medium volatility workloads"
potential_savings: "10-15%"
implementation_effort: "high"
capacity_planning_insights:
current_peak_capacity_requirement: 3200
forecasted_peak_capacity_requirement: 3680
recommended_buffer_percentage: 20
optimal_scaling_strategy: "predictive_with_reactive_backup"
next_steps:
- "Implement scheduled scaling for identified hourly patterns"
- "Set up demand buffering for high-variability components"
- "Create predictive scaling models based on identified trends"
- "Establish regular demand analysis review cycles"Common Challenges and Solutions
Challenge: Insufficient Historical Data
Solution: Start collecting comprehensive metrics immediately. Use synthetic data generation for testing. Implement gradual analysis as more data becomes available. Focus on real-time patterns while building historical datasets.
Challenge: Complex Multi-Component Workloads
Solution: Analyze components individually and in combination. Use correlation analysis to understand dependencies. Implement hierarchical analysis from individual resources to workload level.
Challenge: Seasonal and Irregular Patterns
Solution: Collect data over multiple seasonal cycles. Use advanced statistical methods for pattern detection. Implement flexible models that can adapt to changing patterns.
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 completeness.
Challenge: Translating Analysis to Action
Solution: Create clear, actionable recommendations from analysis. Establish processes for implementing optimization based on findings. Use automation to act on analysis insights.