Fundamentals and Core Concepts

Performance Metrics

These metrics measure how well your system is performing:

1. Latency: Time taken to process a request

   • Target: Depends on SLOs (e.g., p95 < 200ms)
   • Formula: Time request completed - Time request received

2. Throughput: Number of requests processed per unit time

   • Target: Depends on system requirements
   • Formula: Number of requests / Time period

3. Error Rate: Percentage of requests that result in errors

   • Target: Typically <0.1% for critical services
   • Formula: (Number of errors / Total requests) * 100%

4. Saturation: Extent to which a resource has more work than it can handle

   • Target: Avoid saturation (queue depth > 0)
   • Formula: Varies by resource (e.g., queue depth, thread pool utilization)

Business Metrics

These metrics connect technical capacity to business outcomes:

1. User Growth: Rate of increase in user base

   • Formula: (Current users - Previous users) / Previous users * 100%

2. Transaction Volume: Number of business transactions

   • Formula: Sum of transactions in time period

3. Feature Adoption: Usage of specific features

   • Formula: Number of feature uses / Total user sessions

4. Seasonal Patterns: Cyclical variations in demand

   • Formula: Typically analyzed with time series decomposition

Cost Metrics

These metrics help optimize the financial aspects of capacity:

1. Cost per Request: Infrastructure cost divided by request count

   • Formula: Total infrastructure cost / Number of requests

2. Cost per User: Infrastructure cost divided by user count

   • Formula: Total infrastructure cost / Number of users

3. Resource Efficiency: Business value generated per unit of resource

   • Formula: Business value metric / Resource consumption

4. Utilization Efficiency: Actual utilization vs. provisioned capacity

   • Formula: Average utilization / Provisioned capacity

Demand Forecasting Techniques

Accurate demand forecasting is the foundation of effective capacity planning.

Time Series Analysis

Time series analysis examines historical data to identify patterns and project future demand:

1. Moving Averages: Smooths out short-term fluctuations

   def moving_average(data, window):
       return [sum(data[i:i+window]) / window for i in range(len(data) - window + 1)]
   

2. Exponential Smoothing: Gives more weight to recent observations

   def exponential_smoothing(data, alpha):
       result = [data[0]]
       for i in range(1, len(data)):
           result.append(alpha * data[i] + (1 - alpha) * result[i-1])
       return result
   

3. Seasonal Decomposition: Separates time series into trend, seasonal, and residual components

   from statsmodels.tsa.seasonal import seasonal_decompose
   
   def decompose_time_series(data, period):
       result = seasonal_decompose(data, model='multiplicative', period=period)
       return result.trend, result.seasonal, result.resid
   

4. ARIMA Models: Combines autoregression, differencing, and moving averages

   from statsmodels.tsa.arima.model import ARIMA
   
   def arima_forecast(data, order, steps):
       model = ARIMA(data, order=order)
       model_fit = model.fit()
       forecast = model_fit.forecast(steps=steps)
       return forecast
   

Machine Learning Approaches

Machine learning can capture complex patterns and incorporate multiple variables:

1. Linear Regression: Models relationship between demand and influencing factors

   from sklearn.linear_model import LinearRegression
   
   def linear_regression_forecast(X, y, X_future):
       model = LinearRegression()
       model.fit(X, y)
       return model.predict(X_future)
   

2. Random Forest: Captures non-linear relationships and feature interactions

   from sklearn.ensemble import RandomForestRegressor
   
   def random_forest_forecast(X, y, X_future):
       model = RandomForestRegressor(n_estimators=100)
       model.fit(X, y)
       return model.predict(X_future)
   

3. LSTM Networks: Deep learning approach for complex sequential patterns

   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import LSTM, Dense
   
   def create_lstm_model(input_shape):
       model = Sequential()
       model.add(LSTM(50, return_sequences=True, input_shape=input_shape))
       model.add(LSTM(50))
       model.add(Dense(1))
       model.compile(optimizer='adam', loss='mse')
       return model