TCP Timers: The Temporal Guardians of Reliable Communication

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# Original TCP SRTT calculation (RFC 793 style)
def update_srtt_original(srtt: float, rtt_sample: float, alpha: float = 0.875) -> float:
    """
    Update the Smoothed Round-Trip Time using EWMA.
    
    Args:
        srtt: Current smoothed RTT estimate (in ms)
        rtt_sample: New RTT measurement (in ms)
        alpha: Smoothing factor (typically 0.875 = 7/8)
    
    Returns:
        Updated SRTT estimate
    
    Example:
        >>> srtt = 100  # Initial estimate: 100ms
        >>> srtt = update_srtt_original(srtt, 80)   # New sample: 80ms
        >>> print(f"New SRTT: {srtt:.2f}ms")  # SRTT moves slightly toward 80
        New SRTT: 97.50ms
        
        >>> srtt = update_srtt_original(srtt, 120)  # New sample: 120ms  
        >>> print(f"New SRTT: {srtt:.2f}ms")  # SRTT moves toward 120
        New SRTT: 100.31ms
    """
    return alpha * srtt + (1 - alpha) * rtt_sample
 
 
# Simulation: How SRTT evolves over time
def simulate_srtt_evolution():
    """Demonstrate SRTT smoothing over varying RTT samples."""
    import random
    
    # Initial SRTT estimate
    srtt = 100.0  # 100ms initial estimate
    
    # Simulate RTT samples with some variation and a spike
    samples = [95, 102, 98, 105, 97, 150, 103, 99, 101, 98]
    
    print("RTT Measurement Smoothing Simulation")
    print("=" * 50)
    print(f"Initial SRTT estimate: {srtt:.2f}ms")
    print(f"Smoothing factor (α): 0.875 (7/8)")
    print()
    
    for i, sample in enumerate(samples):
        old_srtt = srtt
        srtt = update_srtt_original(srtt, sample)
        delta = srtt - old_srtt
        print(f"Sample {i+1}: RTT={sample:3d}ms → SRTT: {old_srtt:.2f} → {srtt:.2f}ms (Δ={delta:+.2f})")
    
    print()
    print("Notice how the spike (150ms) affects SRTT only gradually,")
    print("preventing overreaction to temporary conditions.")
 
 
if __name__ == "__main__":
    simulate_srtt_evolution()

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class JacobsonRTOEstimator:
    """
    Implements Jacobson's algorithm for RTO estimation (RFC 6298).
    
    This is the algorithm used by modern TCP implementations to calculate
    the retransmission timeout based on RTT measurements.
    """
    
    def __init__(self, clock_granularity: float = 1.0, min_rto: float = 1000.0):
        """
        Initialize the RTO estimator.
        
        Args:
            clock_granularity: Timer granularity in ms (G in RFC 6298)
            min_rto: Minimum RTO value in ms (RFC 6298 recommends 1 second)
        """
        self.srtt: float | None = None      # Smoothed RTT
        self.rttvar: float | None = None    # RTT variance
        self.rto: float = 1000.0            # Current RTO (default 1 second)
        self.clock_granularity = clock_granularity
        self.min_rto = min_rto
        
        # Constants from RFC 6298
        self.alpha = 1/8    # SRTT smoothing factor
        self.beta = 1/4     # RTTVAR smoothing factor
        self.K = 4          # Variance multiplier
    
    def update(self, rtt_sample: float) -> float:
        """
        Update RTO estimate with a new RTT measurement.
        
        Args:
            rtt_sample: A new RTT measurement in milliseconds
            
        Returns:
            The updated RTO value in milliseconds
        """
        if self.srtt is None:
            # First measurement - initialize state
            self.srtt = rtt_sample
            self.rttvar = rtt_sample / 2
        else:
            # Subsequent measurements - apply Jacobson's algorithm
            # Note: RTTVAR must be updated BEFORE SRTT (uses old SRTT value)
            
            # Calculate absolute deviation from current estimate
            deviation = abs(self.srtt - rtt_sample)
            
            # Update RTTVAR: exponentially weighted deviation
            self.rttvar = (1 - self.beta) * self.rttvar + self.beta * deviation
            
            # Update SRTT: exponentially weighted average
            self.srtt = (1 - self.alpha) * self.srtt + self.alpha * rtt_sample
        
        # Calculate new RTO
        # RTO = SRTT + max(G, K * RTTVAR)
        self.rto = self.srtt + max(self.clock_granularity, self.K * self.rttvar)
        
        # Apply minimum RTO (RFC 6298: SHOULD be 1 second)
        self.rto = max(self.rto, self.min_rto)
        
        return self.rto
    
    def get_state(self) -> dict:
        """Return current estimator state for debugging/monitoring."""
        return {
            "srtt": self.srtt,
            "rttvar": self.rttvar,
            "rto": self.rto,
        }
 
 
def demonstrate_jacobson():
    """Demonstrate Jacobson's algorithm with real-world scenarios."""
    
    print("=" * 70)
    print("Jacobson's Algorithm Demonstration")
    print("=" * 70)
    
    # Scenario 1: Stable network
    print("\n📊 SCENARIO 1: Stable Low-Latency Network")
    print("-" * 50)
    estimator = JacobsonRTOEstimator(min_rto=200)  # Lower min for demo
    
    stable_samples = [100, 102, 99, 101, 98, 103, 100, 97, 102, 99]
    
    for i, sample in enumerate(stable_samples):
        rto = estimator.update(sample)
        state = estimator.get_state()
        print(f"Sample {i+1:2d}: RTT={sample:3d}ms | "
              f"SRTT={state['srtt']:.1f}ms | "
              f"RTTVAR={state['rttvar']:.1f}ms | "
              f"RTO={rto:.1f}ms")
    
    print(f"\n✓ Final RTO for stable network: {estimator.rto:.1f}ms")
    print("  Notice: Low RTTVAR leads to tight RTO close to SRTT")
    
    # Scenario 2: Variable network
    print("\n📊 SCENARIO 2: High-Variance Network")
    print("-" * 50)
    estimator = JacobsonRTOEstimator(min_rto=200)
    
    variable_samples = [100, 150, 80, 130, 60, 140, 90, 120, 70, 110]
    
    for i, sample in enumerate(variable_samples):
        rto = estimator.update(sample)
        state = estimator.get_state()
        print(f"Sample {i+1:2d}: RTT={sample:3d}ms | "
              f"SRTT={state['srtt']:.1f}ms | "
              f"RTTVAR={state['rttvar']:.1f}ms | "
              f"RTO={rto:.1f}ms")
    
    print(f"\n✓ Final RTO for variable network: {estimator.rto:.1f}ms")
    print("  Notice: High RTTVAR leads to more conservative RTO")
    
    # Scenario 3: Network with sudden congestion
    print("\n📊 SCENARIO 3: Sudden Congestion Event")
    print("-" * 50)
    estimator = JacobsonRTOEstimator(min_rto=200)
    
    # Start stable, then congestion spike, then recovery
    congestion_samples = [100, 98, 102, 99, 250, 300, 280, 150, 110, 102]
    
    for i, sample in enumerate(congestion_samples):
        rto = estimator.update(sample)
        state = estimator.get_state()
        marker = " ← congestion!" if 250 <= sample <= 300 else ""
        print(f"Sample {i+1:2d}: RTT={sample:3d}ms | "
              f"SRTT={state['srtt']:.1f}ms | "
              f"RTTVAR={state['rttvar']:.1f}ms | "
              f"RTO={rto:.1f}ms{marker}")
    
    print(f"\n✓ Algorithm adapts to congestion and recovers gradually")
 
 
if __name__ == "__main__":
    demonstrate_jacobson()

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def calculate_backoff_schedule(initial_rto: float, max_retries: int = 6, 
                                  max_rto: float = 120000.0) -> list[dict]:
    """
    Calculate the exponential backoff schedule for TCP retransmissions.
    
    Args:
        initial_rto: Initial RTO value in milliseconds
        max_retries: Maximum number of retransmission attempts
        max_rto: Maximum RTO cap in milliseconds (default 120 seconds)
    
    Returns:
        List of dictionaries with retry info
    """
    schedule = []
    current_rto = initial_rto
    cumulative_time = 0
    
    for retry in range(max_retries + 1):
        schedule.append({
            "attempt": retry,
            "rto_ms": current_rto,
            "rto_seconds": current_rto / 1000,
            "cumulative_ms": cumulative_time,
            "cumulative_seconds": cumulative_time / 1000,
        })
        
        cumulative_time += current_rto
        current_rto = min(current_rto * 2, max_rto)  # Double with cap
    
    return schedule
 
 
def demonstrate_backoff():
    """Show TCP exponential backoff in action."""
    
    print("TCP Retransmission Exponential Backoff")
    print("=" * 70)
    print()
    
    # Typical internet RTO scenario
    initial_rto = 1000  # 1 second (RFC 6298 minimum)
    schedule = calculate_backoff_schedule(initial_rto, max_retries=6)
    
    print("Scenario: Typical Internet connection (RTO starts at 1 second)")
    print("-" * 70)
    print(f"{'Attempt':<10} {'RTO':<15} {'Cumulative Wait':<20} {'Status'}")
    print("-" * 70)
    
    for entry in schedule:
        attempt = entry['attempt']
        rto = entry['rto_seconds']
        cumulative = entry['cumulative_seconds']
        
        if attempt == 0:
            status = "Initial transmission"
        elif attempt <= 2:
            status = "Probably recoverable"
        elif attempt <= 4:
            status = "Significant delay"
        else:
            status = "Connection likely dead"
        
        print(f"{attempt:<10} {rto:>10.1f}s    {cumulative:>10.1f}s           {status}")
    
    total_time = schedule[-1]['cumulative_seconds'] + schedule[-1]['rto_seconds']
    print("-" * 70)
    print(f"Total time before giving up: {total_time:.0f} seconds ({total_time/60:.1f} minutes)")
    
    print()
    print("Key Observations:")
    print("• Each retry doubles the timeout (exponential growth)")
    print("• After 6 retries, total wait time exceeds 2 minutes")
    print("• This prevents TCP from hammering a failing path")
    print("• Applications should implement their own timeouts for responsiveness")
 
 
if __name__ == "__main__":
    demonstrate_backoff()

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from dataclasses import dataclass
from enum import Enum
from typing import Optional
 
class SegmentState(Enum):
    SENT = "sent"
    RETRANSMITTED = "retransmitted"
    ACKNOWLEDGED = "acknowledged"
 
@dataclass
class TrackedSegment:
    """Track state of a sent segment for RTT measurement."""
    sequence_number: int
    first_send_time: float
    retransmit_count: int = 0
    state: SegmentState = SegmentState.SENT
 
 
class TCPRTOManagerWithKarn:
    """
    TCP RTO manager implementing Karn's algorithm.
    
    Demonstrates how RTT measurements are handled with
    proper ambiguity resolution.
    """
    
    def __init__(self):
        self.srtt: Optional[float] = None
        self.rttvar: Optional[float] = None
        self.rto: float = 1000.0  # Initial 1 second
        self.backed_off_rto: float = 1000.0  # Current operational RTO
        
        # Track outstanding segments
        self.segments: dict[int, TrackedSegment] = {}
        
        # Statistics
        self.rtt_samples_used = 0
        self.rtt_samples_discarded = 0
    
    def segment_sent(self, seq: int, send_time: float):
        """Record a segment being sent for the first time."""
        self.segments[seq] = TrackedSegment(
            sequence_number=seq,
            first_send_time=send_time
        )
    
    def segment_retransmitted(self, seq: int, retransmit_time: float):
        """Record a segment being retransmitted (timeout occurred)."""
        if seq in self.segments:
            seg = self.segments[seq]
            seg.retransmit_count += 1
            seg.state = SegmentState.RETRANSMITTED
            
            # Apply exponential backoff to operational RTO
            self.backed_off_rto = min(self.backed_off_rto * 2, 120000)
            
            print(f"⚠️  Segment {seq} retransmitted (attempt #{seg.retransmit_count})")
            print(f"    Backed-off RTO: {self.backed_off_rto:.0f}ms")
    
    def ack_received(self, ack: int, recv_time: float) -> Optional[float]:
        """
        Process acknowledgment. Returns RTT if measurement was valid.
        Implements Karn's algorithm for ambiguity handling.
        """
        # Find the acknowledged segment
        acked_seqs = [s for s in self.segments if s < ack]
        
        if not acked_seqs:
            return None
        
        measured_rtt = None
        
        for seq in acked_seqs:
            seg = self.segments[seq]
            
            if seg.retransmit_count > 0:
                # KARN'S ALGORITHM: Do not use this RTT sample
                self.rtt_samples_discarded += 1
                print(f"📊 Segment {seq}: ACK received but RTT DISCARDED (ambiguous)")
                print(f"   Segment was retransmitted - cannot determine which copy was ACKed")
            else:
                # First transmission was acknowledged - safe to measure
                rtt = recv_time - seg.first_send_time
                measured_rtt = rtt
                self._update_rtt_estimates(rtt)
                self.rtt_samples_used += 1
                
                # KARN's ALGORITHM: Reset backed-off RTO only on clean measurement
                self.backed_off_rto = self.rto
                
                print(f"✓ Segment {seq}: RTT measured = {rtt:.1f}ms (used for estimate)")
            
            del self.segments[seq]
        
        return measured_rtt
    
    def _update_rtt_estimates(self, rtt_sample: float):
        """Update SRTT/RTTVAR using Jacobson's algorithm."""
        if self.srtt is None:
            self.srtt = rtt_sample
            self.rttvar = rtt_sample / 2
        else:
            deviation = abs(self.srtt - rtt_sample)
            self.rttvar = 0.75 * self.rttvar + 0.25 * deviation
            self.srtt = 0.875 * self.srtt + 0.125 * rtt_sample
        
        self.rto = self.srtt + max(1, 4 * self.rttvar)
        self.rto = max(self.rto, 200)  # Minimum 200ms for demo
    
    def get_current_rto(self) -> float:
        """Get the current operational RTO (includes backoff)."""
        return self.backed_off_rto
    
    def print_statistics(self):
        """Print measurement statistics."""
        print(f"\n📈 RTT Measurement Statistics:")
        print(f"   Samples used: {self.rtt_samples_used}")
        print(f"   Samples discarded (Karn): {self.rtt_samples_discarded}")
        print(f"   Current SRTT: {self.srtt:.1f}ms" if self.srtt else "   SRTT: Not yet measured")
        print(f"   Current RTO: {self.rto:.1f}ms")
 
 
def demonstrate_karns_algorithm():
    """Demonstrate Karn's algorithm with a realistic scenario."""
    
    print("=" * 70)
    print("Karn's Algorithm Demonstration")
    print("=" * 70)
    print()
    
    manager = TCPRTOManagerWithKarn()
    
    # Scenario: Mix of successful and retransmitted segments
    print("Timeline of events:")
    print("-" * 50)
    
    # Segment 1: Successful first transmission
    print("\nT=0ms:    Send Segment 1000")
    manager.segment_sent(1000, 0)
    
    # Segment 2: Will need retransmission
    print("T=50ms:   Send Segment 1500")
    manager.segment_sent(1500, 50)
    
    # ACK for segment 1 arrives
    print("T=120ms:  ACK 1500 received")
    manager.ack_received(1500, 120)
    
    # Segment 2 times out
    print("T=1100ms: Timeout for Segment 1500")
    manager.segment_retransmitted(1500, 1100)
    
    # Segment 3 sent during backoff period
    print("T=1200ms: Send Segment 2000")
    manager.segment_sent(2000, 1200)
    
    # ACK for retransmitted segment 2
    print("T=1400ms: ACK 2000 received")
    manager.ack_received(2000, 1400)
    
    # ACK for segment 3 (clean measurement)
    print("T=1350ms: ACK 2500 received")
    manager.ack_received(2500, 1350)
    
    manager.print_statistics()
 
 
if __name__ == "__main__":
    demonstrate_karns_algorithm()