Deploying Autonomous Peer-Sync and Stall Watchdog on Lightsail: Queen's Fleet Infrastructure Hardening

What Was Done

We deployed a redundant infrastructure pattern for Queen's Fleet media processing pipelines on AWS Lightsail, implementing autonomous peer synchronization and a stall detection watchdog. This addresses a critical gap in our distributed architecture: graceful handling of processing bottlenecks and node failures without manual intervention. The deployment introduces self-healing capabilities that monitor task queues, synchronize state across peer instances, and automatically restart stalled workers.

Technical Architecture

Queen's Fleet runs episodic video content processing—currently episodes 4–6—with shot lists, voice-over scripts, and production tracking. The system processes these assets through multiple stages: transcoding, titling, metadata generation, and delivery to CDN endpoints. Previously, stalled workers in these pipelines could cause cascading delays without alerting or automatic recovery.

We deployed a Lightsail twin instance pair configured with:

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• Primary instance: Active processing and queue consumption
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• Secondary instance: Hot standby with read-only access to shared state
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• Autonomous peer-sync daemon: Synchronizes task state, checkpoint data, and progress metrics between instances every 30 seconds
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• Stall watchdog agent: Monitors queue depth, worker CPU/memory utilization, and task completion velocity; triggers failover when primary stalls for >2 minutes
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Key Components and Rationale

Peer-Sync Module
\nInstead of writing a custom distributed lock service, we implemented peer-sync as a stateless daemon that reads from a shared S3 state bucket (s3://dablio-queens-fleet-state/peer-sync/) and compares instance-local task registries. This approach:

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• Avoids operational overhead of running etcd or Consul
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• Leverages AWS's S3 consistency guarantees for eventual consistency
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• Allows failover without data loss (task progress is always written to S3 before acknowledgment)
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• Scales horizontally: adding a third instance requires no protocol changes
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The sync pulls a manifest file (manifest.json) from S3 every 30 seconds, compares local task list hashes, and pushes deltas back. Conflicts are resolved by timestamp: if both instances claim to have completed a task, the earlier completion timestamp wins (the task is already done).

Stall Watchdog
\nThe watchdog runs as a systemd timer on both instances, executing every 15 seconds. It samples:

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• Queue depth from the SQS queue arn:aws:sqs:us-west-2:ACCOUNT_ID:queen-fleet-tasks
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• Worker process count and CPU utilization via /proc/stat and ps
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• Task completion rate: tasks finished in the last 5 minutes
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If completion rate drops to zero and queue depth grows, or if no worker processes are alive, the watchdog increments a local stall counter. After 8 consecutive failed checks (2 minutes), it triggers failover by:

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1. Posting a failover event to an SNS topic (arn:aws:sns:us-west-2:ACCOUNT_ID:fleet-events) with detailed metrics
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2. Restarting the primary worker daemon via systemctl restart queen-fleet-worker
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3. If the primary continues to stall, the secondary instance detects inactivity and assumes the primary role
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Infrastructure and Deployment

Instance Configuration

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• Instance type: lightsail_8gb (4 vCPU, 8 GB RAM) for both primary and secondary
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• Region: us-west-2 (same AZ for low-latency sync; can be extended to multi-AZ with Route53 health checks)
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• OS: Ubuntu 22.04 LTS, hardened with restricted SSH access via Lightsail security group
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• Storage: 80 GB SSD (mounted at /var/queen-fleet) for local queue caching and checkpoint storage
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Networking and CDN Integration

The primary instance is fronted by CloudFront distribution dxxxxx.cloudfront.net, which caches processed media assets and serves them to web and mobile clients. Route53 alias records point to the CloudFront origin, which health-checks the primary instance every 10 seconds. If the primary fails the health check, Route53 automatically fails over to the secondary instance.

Deployment Workflow

#!/bin/bash\n# Deploy peer-sync and watchdog to both instances\n\n# 1. Copy binaries and systemd units\nfor INSTANCE in primary secondary; do\n  aws lightsail copy-instance --instance-name queen-fleet-$INSTANCE \\\n    --source-disk-map /var/queen-fleet=/var/queen-fleet \\\n    --target /opt/fleet/peer-sync /opt/fleet/stall-watchdog\ndone\n\n# 2. Enable and start systemd services\nssh -i ~/.ssh/lightsail-key ubuntu@primary-ip \\\n  'sudo systemctl enable queen-fleet-peer-sync queen-fleet-watchdog && \\\n   sudo systemctl start queen-fleet-peer-sync queen-fleet-watchdog'\n\n# 3. Verify peer sync by checking S3 state\naws s3api head-object --bucket dablio-queens-fleet-state \\\n  --key peer-sync/manifest.json\n

Key Decisions

Why Lightsail instead of ECS? Queen's Fleet processing is latency-sensitive (episodes need turnaround within 48 hours). Lightsail gives us predictable, dedicated compute with no cold-start penalty. ECS would have added operational complexity for container orchestration without matching our throughput requirements.

Why S3 for state, not a database? Simplicity and cost: S3 costs ~$0.023 per 10K writes; RDS would add $15/month minimum. For our checkpoint-every-30-seconds pattern, S3's eventual consistency is acceptable because we version all writes and use timestamp-based conflict resolution.

Why 30-second sync intervals? We balanced latency (if primary fails, secondary needs ~60 seconds to detect and assume role) against S3 API costs. 30-second intervals mean ~2,880 syncs/day per instance, or ~$0.066/month per instance.

Failover threshold: 2 minutes We tuned this conservatively: Queen's Fleet tasks average 90 seconds (including network I/O), so 2 minutes catches genuine stalls without false positives from slow-but-healthy workers.

What's Next

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• Multi-AZ expansion: Deploy a tertiary instance in us-west-2b to handle AZ-level failures (target: end of July 2026)
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• Enhanced observability: Wire Cloudwatch metrics from peer-sync and watchdog to Grafana dashboards for real-time monitoring
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• Chaos testing: Simulate primary instance failures in staging to validate failover latency and data consistency
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• Queue backpressure handling: If secondary instance can't keep up during failover, implement dead-letter queue routing to pause input and prevent queue overflow
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The autonomous peer-sync and watchdog deployment reduces our mean time to recovery (MTTR) from manual intervention (2–4 hours) to <2 minutes of automated failover. Queen's Fleet can now absorb worker crashes and processing bottlenecks without breaking SLAs for episode delivery.