Class: Semian::Simple::PIDController

Inherits:
Object
  • Object
show all
Defined in:
lib/semian/pid_controller.rb

Overview

PID Controller for adaptive circuit breaking Based on the error function: P = (error_rate - ideal_error_rate) - (1 - (error_rate - ideal_error_rate)) * rejection_rate Note: P increases when error_rate increases

P decreases when rejection_rate increases (providing feedback)

Direct Known Subclasses

ThreadSafe::PIDController

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(kp:, ki:, kd:, window_size:, sliding_interval:, implementation:, initial_error_rate:, dead_zone_ratio:, ideal_error_rate_estimator_cap_value:, integral_upper_cap:, integral_lower_cap:) ⇒ PIDController

Returns a new instance of PIDController.



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# File 'lib/semian/pid_controller.rb', line 16

def initialize(kp:, ki:, kd:, window_size:, sliding_interval:, implementation:, initial_error_rate:,
  dead_zone_ratio:, ideal_error_rate_estimator_cap_value:, integral_upper_cap:, integral_lower_cap:)
  @kp = kp
  @ki = ki
  @kd = kd
  @dead_zone_ratio = dead_zone_ratio
  @integral_upper_cap = integral_upper_cap
  @integral_lower_cap = integral_lower_cap

  @rejection_rate = 0.0
  @integral = 0.0
  @derivative = 0.0
  @previous_p_value = 0.0
  @last_ideal_error_rate = initial_error_rate

  @window_size = window_size
  @sliding_interval = sliding_interval
  @smoother = SimpleExponentialSmoother.new(
    cap_value: ideal_error_rate_estimator_cap_value,
    initial_value: initial_error_rate,
    observations_per_minute: 60 / sliding_interval,
  )

  @errors = implementation::SlidingWindow.new(max_size: 200 * window_size)
  @successes = implementation::SlidingWindow.new(max_size: 200 * window_size)
  @rejections = implementation::SlidingWindow.new(max_size: 200 * window_size)

  @last_error_rate = 0.0
  @last_p_value = 0.0
end

Instance Attribute Details

#rejection_rate ⇒ Object (readonly)

Returns the value of attribute rejection_rate.



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# File 'lib/semian/pid_controller.rb', line 14

def rejection_rate
  @rejection_rate
end

Instance Method Details

#metrics(full: true) ⇒ Object

Get current metrics for monitoring/debugging



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# File 'lib/semian/pid_controller.rb', line 110

def metrics(full: true)
  result = {
    rejection_rate: @rejection_rate,
    error_rate: @last_error_rate,
    ideal_error_rate: @last_ideal_error_rate,
    dead_zone_ratio: @dead_zone_ratio,
    p_value: @last_p_value,
    previous_p_value: @previous_p_value,
    integral: @integral,
    derivative: @derivative,
  }

  if full
    result[:smoother_state] = @smoother.state
    result[:current_window_requests] = {
      success: @successes.size,
      error: @errors.size,
      rejected: @rejections.size,
    }
  end

  result
end

#record_request(outcome) ⇒ Object 



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# File 'lib/semian/pid_controller.rb', line 47

def record_request(outcome)
  case outcome
  when :error
    @errors.push(current_time)
  when :success
    @successes.push(current_time)
  when :rejected
    @rejections.push(current_time)
  end
end

#reset ⇒ Object

Reset the controller state



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# File 'lib/semian/pid_controller.rb', line 95

def reset
  @rejection_rate = 0.0
  @integral = 0.0
  @previous_p_value = 0.0
  @derivative = 0.0
  @last_p_value = 0.0
  @errors.clear
  @successes.clear
  @rejections.clear
  @last_error_rate = 0.0
  @smoother.reset
  @last_ideal_error_rate = @smoother.forecast
end

#should_reject? ⇒ Boolean

Should we reject this request based on current rejection rate?

Returns:

  • (Boolean) 


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# File 'lib/semian/pid_controller.rb', line 90

def should_reject?
  rand < @rejection_rate
end

#update ⇒ Object 



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# File 'lib/semian/pid_controller.rb', line 58

def update
  # Store the last window's P value so that we can serve it up in the metrics snapshots
  @previous_p_value = @last_p_value

  @last_error_rate = calculate_error_rate

  store_error_rate(@last_error_rate)

  dt = @sliding_interval

  @last_p_value = calculate_p_value(@last_error_rate)

  proportional = @kp * @last_p_value
  @integral += @last_p_value * dt
  integral = @ki * @integral
  @derivative = @kd * (@last_p_value - @previous_p_value) / dt

  # Calculate the control signal (change in rejection rate)
  control_signal = proportional + integral + @derivative

  # Calculate what the new rejection rate would be
  new_rejection_rate = @rejection_rate + control_signal

  # Update rejection rate (clamped between 0 and 1)
  @rejection_rate = new_rejection_rate.clamp(0.0, 1.0)

  @integral = @integral.clamp(@integral_lower_cap, @integral_upper_cap)

  @rejection_rate
end