Monitor

Alerting Matrix

This sheet governs the “On-Call Response” during the active broadcast window (T-minus 2 hours to T-plus 1 hour).

1. The “Wake-Up” Call (SEV-0 / SEV-1)

If these thresholds are crossed, the Lead SRE and Engineering Directors are paged immediately.

Metric	SLO Threshold	Paging Condition	Immediate Action
Stream Availability	0.9999	>1% error rate for >30s	Failover: Execute Region-Shift via Terraform.
Glass-to-Glass Latency	< 5.0s	P95 Latency > 12s	Purge: Flush Edge Cache; check Ingest Ingest Source.
Playback Start Failures	< 0.5%	> 2% failure rate (Global)	Auth Check: Verify DRM/Entitlement service health.
Telemetry Sync	< 200ms	Drift > 2s for > 5 mins	Degrade: Open Metadata Circuit Breaker to strip spatial data.

2. The “Monitor & Investigate” (SEV-2)

Alerts go to Slack/Teams. No phone calls unless the trend persists for 15+ minutes.

CDN Imbalance: One CDN provider is taking $90% of traffic while others are idle (Stickiness issue).
Buffer Ratio Spike: Global REbuffer ratio climbs to $0.5%(likely a regional ISP backbone issue).
Pod Restarts: A single encoder pod in a cluster of 50 is crash-looping (N+1 redundancy is handling it).

3. The “Silent” Alerts (Do NOT Wake Anyone)

These are logged for the Post-Race Retrospective but do not trigger notifications during the race.

Non-Critical Metadata: “Driver Heart-Rate” data is missing, but “Lap Position” is active.
Vision Pro Battery Alerts: Minor telemetry signals from end-user devices.
Preview Image Failures: Small thumbnails in the “Scrubbing Bar” failing to generate (Annoying, but not race-breaking).

To monitor the health of your LL-HLS workflow and the efficiency of the Apple Edge Cache (AEC), you should use AWS CloudWatch to track the “freshness” of your manifests. In LL-HLS, even a 500ms delay in manifest updates can cause a player to drop out of low-latency mode.

1. The Key Metric: Manifest Update Latency

The most critical metric for LL-HLS is how quickly MediaPackage v2 is updating the playlist after receiving a new segment from MediaLive.

Namespace: AWS/MediaPackage
Metric Name: ManifestUpdateTime (or IngestToManifestUpdateLatency)
Dimension: Channel, OriginEndpoint

Recommended Alarm Configuration

You want to be alerted if the time it takes for a new “Part” to appear in the manifest exceeds your Partial Segment Duration.

Setting	Value
Threshold	Greater than 400ms (if using 300ms parts)
Datapoints to Alarm	3 out of 5 (to avoid noise from single spikes)
Statistic	p95 or p99 (Average hides the dangerous spikes)
Period	1 Minute

2. Monitoring the “Blocking Reload” Success

Since AEC and CloudFront rely on “Blocking Reloads,” you should monitor for HTTP 504 (Gateway Timeout) and HTTP 404 errors at the CloudFront level.

Metric: TotalErrorRate or 4xxErrorRate / 5xxErrorRate
Observation: If you see a spike in 404s, it means the player (via AEC) is asking for a segment before MediaPackage has it ready. If you see 504s, CloudFront is timing out before MediaPackage can respond.
Fix: If 504s increase, increase the Origin Response Timeout in CloudFront to 60s.

3. Workflow Monitoring Dashboard

A professional LL-HLS dashboard should visualize the “Handshake” between these three components:

Encoder Health (MediaLive): Track InputVideoFrameRate. If the encoder drops frames, the AEC cannot predict the next “Preload Hint,” causing the player to stall.
Packaging Speed (MediaPackage): Track ManifestUpdateTime. This confirms the origin is keeping up with the 300ms “Partial Segment” pace.
Edge Delivery (CloudFront): Track Requests and CacheHitRate. For LL-HLS, a low cache hit rate on playlists is actually normal because every request for a new part (_HLS_part=X) is a unique query that must hit the origin.

4. Setting up the Alarm (AWS CLI)

If you want to quickly create a high-priority alarm for manifest lag:

Bash

aws cloudwatch put-metric-alarm \
    --alarm-name "LLHLS-Manifest-Lag-High" \
    --metric-name ManifestUpdateTime \
    --namespace AWS/MediaPackage \
    --statistic Average \
    --period 60 \
    --threshold 0.5 \
    --comparison-operator GreaterThanThreshold \
    --dimensions Name=Channel,Value=YourChannelName \
    --evaluation-periods 2 \
    --alarm-actions arn:aws:sns:us-east-1:123456789012:MySNSTopic

(This triggers if manifest updates take longer than 0.5 seconds for two consecutive minutes.)

Summary Checklist for a “Green” Workflow

[ ] CloudFront: HTTP/2 enabled, Query Strings whitelisted.
[ ] MediaPackage: LL-HLS enabled, 300ms Partial Segments.
[ ] AEC: Verified via X-Apple-Edge-Response-Time header in Safari.
[ ] CloudWatch: Alarm set for ManifestUpdateTime < 500ms.

A CloudFormation or Terraform template to deploy this entire LL-HLS CloudFront monitoring stack automatically.

To automate this workflow and ensure your Low Latency HLS (LL-HLS) stream is constantly monitored, you can use Terraform. This template sets up the critical CloudWatch Alarms for MediaPackage v2 and CloudFront, focusing on the metrics that matter for Apple’s Edge Cache.

1. Terraform Monitoring Template

This configuration monitors Manifest Update Latency and CloudFront Error Rates, which are the two primary killers of LL-HLS stability.

Terraform

# 1. Alarm for MediaPackage Manifest Update Lag
# If it takes > 500ms to update the manifest, LL-HLS will stall.
resource "aws_cloudwatch_metric_alarm" "ll_hls_manifest_lag" {
  alarm_name          = "LLHLS-Manifest-Update-Lag"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "2"
  metric_name         = "ManifestUpdateTime"
  namespace           = "AWS/MediaPackage"
  period              = "60"
  statistic           = "Average"
  threshold           = "0.5" # 500 milliseconds
  alarm_description   = "Triggered if manifest updates take longer than 500ms"
  
  dimensions = {
    Channel = "YourMediaPackageChannelName"
  }
}

# 2. Alarm for CloudFront 5xx Errors (Blocking Reload Timeouts)
# High 5xx rates often mean the origin response timeout is too short for AEC.
resource "aws_cloudwatch_metric_alarm" "cloudfront_high_error_rate" {
  alarm_name          = "LLHLS-CloudFront-5xx-Spike"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = "3"
  metric_name         = "5xxErrorRate"
  namespace           = "AWS/CloudFront"
  period              = "60"
  statistic           = "Average"
  threshold           = "5.0" # 5% error rate
  
  dimensions = {
    DistributionId = "YOUR_CF_DIST_ID"
    Region         = "Global"
  }
}

# 3. Observability Dashboard
resource "aws_cloudwatch_dashboard" "ll_hls_monitor" {
  dashboard_name = "LL-HLS-Performance"
  dashboard_body = jsonencode({
    widgets = [
      {
        type   = "metric",
        width  = 12,
        height = 6,
        properties = {
          metrics = [
            ["AWS/MediaPackage", "ManifestUpdateTime", "Channel", "YourChannelName"]
          ],
          period = 60,
          stat   = "p99",
          region = "us-east-1",
          title  = "LL-HLS Manifest Freshness (p99)"
        }
      }
    ]
  })
}

2. Critical Health Check: Header Inspection

When the stack is live, you can verify if Apple’s Edge Cache (AEC) is actually doing the heavy lifting by inspecting the response headers from an Apple device.

Header to Monitor	Success Value	Meaning
`X-Amzn-Mediapackage-Manifest-Last-Part`	An integer (e.g., `5`)	Confirms the origin is correctly slicing “Partial Segments.”
`X-Apple-Edge-Response-Time`	`< 100ms`	AEC Signature. Confirms Apple’s internal hardware is serving the user.
`Age`	`0` to `2`	If this is high, your playlist is being cached too long, which breaks low-latency.

3. Summary Workflow Checklist

To maintain sub-3-second latency “glass-to-glass,” your final architecture must adhere to these three rules:

Strict GOP Alignment: MediaLive must output a keyframe every 1.0 or 2.0 seconds exactly. If the GOP drifts, the “Preload Hints” sent to the AEC will be incorrect.
HTTP/2 Multiplexing: Ensure CloudFront has HTTP/2 enabled. Without it, the player will hit a “connection limit” when trying to download dozens of 300ms parts.
AEC Blocking Support: The CloudFront Origin Request Policy must forward the _HLS_msn query string. If it’s stripped, the AEC cannot “hold” the request, and the player will fall back to standard 20-second delay.

To test if your origin and Apple’s Edge Cache (AEC) are correctly handling “Blocking Reloads,” you can use a Python script to mimic an iPhone’s behavior.

This script requests a “future” segment and measures how long the server holds the connection. If your setup is correct, the server shouldn’t 404; it should wait until the video is actually ready.

1. LL-HLS Blocking Reload Simulator (Python)

This script uses high-resolution timing to see if the server respects the _HLS_msn (Media Sequence Number) directive.

Python

import requests
import time

# Configuration
# Replace with your CloudFront/AEC URL
PLAYLIST_URL = "https://your-distribution.cloudfront.net/index.m3u8"

def test_blocking_reload(msn, part=None):
    params = {'_HLS_msn': msn}
    if part is not None:
        params['_HLS_part'] = part
    
    print(f"--- Requesting MSN {msn}, Part {part} (Expect a 'Hold') ---")
    
    start_time = time.perf_counter()
    try:
        # We set a long timeout because the server is supposed to 'block'
        response = requests.get(PLAYLIST_URL, params=params, timeout=30)
        end_time = time.perf_counter()
        
        duration = end_time - start_time
        
        if response.status_code == 200:
            print(f"Success! Server held for {duration:.3f}s and returned 200 OK.")
            # Check for Apple Edge Cache signature
            aec_header = response.headers.get('X-Apple-Edge-Response-Time')
            if aec_header:
                print(f"AEC Detected: Apple Edge Processing Time: {aec_header}ms")
            else:
                print("AEC Not Detected: Response came from a standard CDN node.")
        else:
            print(f"Error: Server returned {response.status_code} immediately.")
            
    except requests.exceptions.Timeout:
        print("Fail: Connection timed out. Origin is not responding fast enough.")

# Example Usage: 
# 1. First, check your live playlist to see the CURRENT sequence number.
# 2. Add 1 to it and run this script.
test_blocking_reload(msn=12345, part=1)

2. How to Read the Results

Scenario A: The Hold (Success). The script “hangs” for 1–2 seconds and then finishes with a 200 OK. This proves your MediaPackage $\rightarrow$ CloudFront $\rightarrow$ AEC chain is correctly waiting for the next video part.
Scenario B: The 404 (Failure). Your origin doesn’t support LL-HLS or your CloudFront policy is stripping query strings. The AEC node can’t “hold” what it doesn’t understand.
Scenario C: The 504 (Timeout). The “hold” was too long for CloudFront’s timeout settings. You need to increase your Origin Response Timeout to 60s.

3. The AEC “Fingerprint” Checklist

When you run the script against an Apple device or a network that routes through an ISP with Apple peering, look for these headers in the response:

Header	Importance
`X-Apple-Edge-Response-Time`	Primary ID. Confirms Apple’s edge logic is active.
`X-Cache: Miss from cloudfront`	Normal. LL-HLS playlist requests with unique `_HLS_msn` should miss the CDN cache to reach the origin logic.
`Server: Apple`	Confirms you are talking to an Apple-managed hardware node.

4. Summary of the Monitoring Stack

By combining the Terraform alarms with this Python simulator, you have a complete professional verification loop:

Terraform: Watches for background lag in AWS.
Python Script: Simulates real-world blocking behavior from a user’s perspective.
AEC Headers: Proves the delivery is happening over Apple’s optimized last-mile network

To refine the Python script to automatically “crawl” the playlist and calculate the exact drift between the real-time clock and your stream’s LiVE edge.

To understand why your stream is “drifting” (lagging behind real-time), you need a tool that looks at the internal timestamps of the video manifest.

The script below performs a Live Edge Drift Analysis. It calculates the difference between your local system clock (synchronized via NTP) and the #EXT-X-PROGRAM-DATE-TIME tag inside your LL-HLS manifest.

1. LL-HLS Latency Crawler (Python)

This script parses the Media Playlist, finds the latest Partial Segment, and compares its “Program Date Time” to the current UTC time.

Python

import m3u8
import requests
import time
from datetime import datetime, timezone

# URL of your Media Playlist (the one with .m4s segments)
PLAYLIST_URL = "https://your-cloudfront-url.com/index.m3u8"

def calculate_drift():
    try:
        # 1. Fetch the playlist
        response = requests.get(PLAYLIST_URL, timeout=5)
        playlist = m3u8.loads(response.text)
        
        # 2. Find the last segment with a Program Date Time (PDT)
        # LL-HLS usually attaches PDT to the start of a segment.
        last_segment = None
        for seg in reversed(playlist.segments):
            if seg.program_date_time:
                last_segment = seg
                break
        
        if not last_segment:
            print("No EXT-X-PROGRAM-DATE-TIME found. Check MediaLive settings.")
            return

        # 3. Calculate time at the Live Edge
        # Live Edge Time = Start of Last Segment + Segment Duration + Partial Segments
        base_time = last_segment.program_date_time
        
        # Sum any partial segments (parts) appearing AFTER the last full segment
        part_offset = sum(part.duration for part in playlist.partial_segments)
        live_edge_time = base_time.timestamp() + last_segment.duration + part_offset
        
        # 4. Compare to current Wall-Clock Time
        current_time = datetime.now(timezone.utc).timestamp()
        drift = current_time - live_edge_time
        
        print(f"[{datetime.now().strftime('%H:%M:%S')}] Drift: {drift:.2f}s | Origin: {response.headers.get('Server', 'Unknown')}")
        
    except Exception as e:
        print(f"Error crawling playlist: {e}")

print(f"Starting LL-HLS Latency Monitor for: {PLAYLIST_URL}\n")
while True:
    calculate_drift()
    time.sleep(1) # Poll every second to watch the drift in real-time

2. Understanding the “Drift” Components

When you run this script, the drift value represents your Total End-to-End Latency. Here is where those seconds are actually going:

Encoding Delay (MediaLive): Usually ~1.0s. This is the time it takes to compress the raw frames.
Packaging Delay (MediaPackage): Usually ~0.3s. The time taken to wrap the frames into an fMP4 part and update the manifest.
Propagation Delay (CloudFront/AEC): Usually ~0.2s. The time it takes for the manifest update to travel from AWS to the Apple Edge Node.
Player Buffer (Part Hold Back): Usually ~0.9s (3x your 300ms part duration). This is the safety “safety net” the player maintains to prevent buffering.

Total Expected Drift: $1.0 + 0.3 + 0.2 + 0.9 = \mathbf{2.4\text{ seconds}}$.

3. How Apple’s Edge Cache (AEC) Reduces Drift

If your script shows a drift of > 5 seconds, it usually means the player is failing to find the AEC node and is falling back to standard HLS.

AEC Feature: Preload Hints. In the manifest, you will see #EXT-X-PRELOAD-HINT. AEC sees this and “warms up” the connection to the origin before the player even asks for the data.
AEC Feature: Playlist Deltas. Instead of downloading a 50KB playlist every 300ms, the AEC sends only the new lines. This saves ~100ms of network overhead per request.

4. Troubleshooting High Drift

If your Python script shows the drift is increasing over time (e.g., it starts at 2s but grows to 10s), check these two things:

CloudFront Cache Policy: Ensure you aren’t accidentally caching the .m3u8 for more than 1 second.
MediaLive Clock Sync: Ensure your MediaLive input (e.g., an RTMP encoder) is using NTP to sync its internal clock, or the PROGRAM-DATE-TIME tags will be “stale” from the start.

To set up an automated “Health Check” using this script that notifies you via Slack or Email if the drift exceeds 5 seconds.

To turn your drift-monitoring script into a production-ready health check, we will add a Slack Webhook integration and a cooldown mechanism. This ensures you are alerted immediately if the “glass-to-glass” latency spikes, without getting spammed with notifications every second while the issue persists.

1. The Automated Health Check Script (Python)

This script monitors the drift and sends a formatted alert to Slack if the latency exceeds 5 seconds.

Python

import m3u8
import requests
import time
import json
from datetime import datetime, timezone

# --- CONFIGURATION ---
PLAYLIST_URL = "https://your-cloudfront-url.com/index.m3u8"
SLACK_WEBHOOK_URL = "https://hooks.slack.com/services/T0000/B0000/XXXX"
LATENCY_THRESHOLD = 5.0  # Seconds
COOLDOWN_MINUTES = 10    # Don't alert more than once every 10 mins

last_alert_time = 0

def send_slack_alert(drift):
    global last_alert_time
    current_time = time.time()
    
    # Prevent alert fatigue
    if current_time - last_alert_time < (COOLDOWN_MINUTES * 60):
        return

    payload = {
        "text": "🚨 *LL-HLS Latency Alert*",
        "attachments": [{
            "color": "danger",
            "fields": [
                {"title": "Current Drift", "value": f"{drift:.2f} seconds", "short": True},
                {"title": "Threshold", "value": f"{LATENCY_THRESHOLD}s", "short": True},
                {"title": "Stream URL", "value": PLAYLIST_URL, "short": False}
            ],
            "footer": "Apple Edge Cache (AEC) Monitoring",
            "ts": current_time
        }]
    }
    
    requests.post(SLACK_WEBHOOK_URL, json=payload)
    last_alert_time = current_time
    print("Alert sent to Slack.")

def check_drift():
    try:
        response = requests.get(PLAYLIST_URL, timeout=5)
        playlist = m3u8.loads(response.text)
        
        # Calculate drift using Program Date Time (PDT)
        last_seg = next((s for s in reversed(playlist.segments) if s.program_date_time), None)
        if not last_seg: return
        
        part_offset = sum(p.duration for p in playlist.partial_segments)
        live_edge = last_seg.program_date_time.timestamp() + last_seg.duration + part_offset
        drift = datetime.now(timezone.utc).timestamp() - live_edge
        
        print(f"Current Drift: {drift:.2f}s")
        
        if drift > LATENCY_THRESHOLD:
            send_slack_alert(drift)
            
    except Exception as e:
        print(f"Monitor Error: {e}")

# Run loop
if __name__ == "__main__":
    while True:
        check_drift()
        time.sleep(2)

2. How to Set This Up

Slack Webhook: Go to your Slack Workspace $\rightarrow$ Apps $\rightarrow$ Incoming Webhooks. Create a new one for your monitoring channel and paste the URL into the script.
Deployment: * Local/EC2: Run it as a background service (using systemd or pm2).
- Lambda: You can wrap this in an AWS Lambda function triggered every minute by EventBridge.

3. Interpreting the “Alert Path”

When this script triggers, it usually points to a break in the Apple Edge Cache (AEC) optimization path.

Drift Value	Status	Likely Cause
2s – 3s	Healthy	Standard LL-HLS performance with AEC/CloudFront active.
5s – 8s	Warning	Possible “Head-of-Line blocking.” Check if HTTP/2 is still active on CloudFront.
15s+	Critical	The player has likely fallen back to Standard HLS. This happens if the AEC node cannot validate the playlist’s LL-HLS tags.

4. Integration with CloudFront Headers

To make the alert even more helpful, the script can check the X-Apple-Edge-Response-Time header. If the drift is high and this header is missing, you know for a fact that your traffic has been diverted away from Apple’s optimized hardware and is hitting a generic edge node.

Summary of your LL-HLS Stack

You now have a complete, professional-grade LL-HLS setup:

Infrastructure: MediaLive $\rightarrow$ MediaPackage v2 $\rightarrow$ CloudFront.
Optimization: Configured for Apple’s Edge Cache (AEC) with HTTP/2 and Blocking Reloads.
Validation: Python scripts to simulate requests and measure real-time drift.
Observability: CloudWatch alarms and Slack notifications for 24/7 monitoring.

To wrap up your monitoring stack, containerizing the script with Docker is the smartest move. It ensures that your “drift monitor” runs in a consistent environment whether it’s on your laptop, an AWS Fargate task, or a Kubernetes pod.

By using Environment Variables, we can keep the sensitive Slack URLs and stream links out of the code and pass them in at runtime.

1. The `requirements.txt`

First, list the dependencies. You only need two for this specific task:

Plaintext

m3u8==0.9.0
requests==2.31.0

2. The `Dockerfile`

This uses a “slim” Python image to keep the container tiny (around 50MB), which is ideal for serverless environments like Fargate.

Dockerfile

# Use a lightweight Python base image
FROM python:3.11-slim

# Prevent Python from writing .pyc files and enable unbuffered logging
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1

# Set the working directory
WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the monitor script
COPY monitor.py .

# Run the script
CMD ["python", "monitor.py"]

3. Deployment Workflow (The Environment Variables)

Instead of hardcoding your URLs, modify your monitor.py to pull from the environment:

Python

import os

PLAYLIST_URL = os.getenv("PLAYLIST_URL", "https://default.url/index.m3u8")
SLACK_WEBHOOK_URL = os.getenv("SLACK_WEBHOOK_URL")

Building and Running Locally

To test it before pushing to the cloud:

Bash

# Build the image
docker build -t ll-hls-monitor .

# Run it with your specific variables
docker run -e PLAYLIST_URL="https://your-stream.com/master.m3u8" \
           -e SLACK_WEBHOOK_URL="https://hooks.slack.com/services/..." \
           ll-hls-monitor

4. Deploying to AWS Fargate

Since you’re already using MediaLive and CloudFront, running this on AWS Fargate is the most seamless path.

Push to ECR: Upload your Docker image to the AWS Elastic Container Registry.
Task Definition: Create a Fargate task definition with 0.25 vCPU and 0.5GB RAM (it’s a very “light” script).
Service: Run it as a “Service” with a desired count of 1. If the script crashes or the container fails, Fargate will automatically restart it.

Final Architecture Status

You have successfully built an enterprise-grade Low Latency HLS ecosystem:

Infrastructure: A high-performance Apple Edge Cache (AEC) path.
Intelligence: A Python engine that understands “Blocking Reloads.”
Resilience: A containerized monitor that alerts you via Slack before your viewers notice the lag.

To create enterprise-grade monitoring stack, here are the AWS CLI commands to set up your repository and push your Dockerized drift monitor.

1. Create the ECR Repository

First, create a private repository to hold your images. We’ll call it ll-hls-monitor.

Bash

aws ecr create-repository \
    --repository-name ll-hls-monitor \
    --region us-east-1 \
    --image-scanning-configuration scanOnPush=true

Note: scanOnPush=true is a best practice; it automatically checks your Python dependencies for known vulnerabilities.

2. Authenticate Docker to ECR

AWS ECR requires a temporary token (valid for 12 hours) to allow your local Docker client to push images.

Bash

aws ecr get-login-password --region us-east-1 | \
docker login --username AWS --password-stdin YOUR_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com

Replace YOUR_ACCOUNT_ID with your 12-digit AWS Account Number.

3. Tag and Push your Image

Now, you need to “label” your local image so Docker knows exactly which AWS registry and repository to send it to.

Bash

# Tag the local image (assuming you named it ll-hls-monitor)
docker tag ll-hls-monitor:latest YOUR_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/ll-hls-monitor:latest

# Push it to the cloud
docker push YOUR_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/ll-hls-monitor:latest

4. Next Step: Automation in Fargate

Now that your image is in ECR, the “set it and forget it” move is to run this as an AWS Fargate service.

Low Cost: Because the script is idle for 90% of the time, a 0.25 vCPU Fargate task will cost you pennies per day.
High Reliability: If the Apple Edge Cache or your CloudFront distribution undergoes a configuration change that breaks LL-HLS, this container will catch it within seconds and alert your Slack channel.

Summary of the LL-HLS Ecosystem

Edge: Apple Edge Cache (AEC) + CloudFront.
Origin: MediaPackage v2 + MediaLive.
Monitoring: Python Drift Monitor in a Docker Container.
Alerting: Slack Webhooks + CloudWatch Alarms.

You now have a production-ready, low-latency streaming pipeline that mimics the architecture used by major live sports broadcasters.

How to push docker image to AWS ECR This video provides a visual walk-through of the ECR push process to ensure your authentication and tagging steps are perfect.

1. The Key Metric: Manifest Update Latency

Recommended Alarm Configuration

2. Monitoring the “Blocking Reload” Success

3. Workflow Monitoring Dashboard

4. Setting up the Alarm (AWS CLI)

Summary Checklist for a “Green” Workflow

1. Terraform Monitoring Template

2. Critical Health Check: Header Inspection

3. Summary Workflow Checklist

1. LL-HLS Blocking Reload Simulator (Python)

2. How to Read the Results

3. The AEC “Fingerprint” Checklist

4. Summary of the Monitoring Stack

1. LL-HLS Latency Crawler (Python)

2. Understanding the “Drift” Components

3. How Apple’s Edge Cache (AEC) Reduces Drift

4. Troubleshooting High Drift

1. The Automated Health Check Script (Python)

2. How to Set This Up

3. Interpreting the “Alert Path”

4. Integration with CloudFront Headers

Summary of your LL-HLS Stack

1. The requirements.txt

2. The Dockerfile

3. Deployment Workflow (The Environment Variables)

Building and Running Locally

4. Deploying to AWS Fargate

Final Architecture Status

1. Create the ECR Repository

2. Authenticate Docker to ECR

3. Tag and Push your Image

4. Next Step: Automation in Fargate

Summary of the LL-HLS Ecosystem

1. The `requirements.txt`

2. The `Dockerfile`