Taming SSM Orchestration Logs: An Automated Disk Space Solution


Introduction

AWS Systems Manager (SSM) plays a critical role in managing EC2 instances at scale—handling automation, orchestration, and remote sessions seamlessly. However, over time, orchestration session logs can accumulate significantly on instances, consuming valuable disk space and potentially impacting system stability if left unmanaged.

This blog outlines a practical and automated retention strategy to manage SSM orchestration logs by compressing older session data and enforcing a defined cleanup policy—ensuring optimal disk utilization while maintaining necessary operational history.


Objective

The primary goal of this implementation is to:

  • Optimize disk usage on EC2 instances

  • Retain recent orchestration data for troubleshooting and audit needs

  • Automate cleanup without impacting SSM agent functionality


Retention Policy

  • Target Path: /var/lib/amazon/ssm/<instance-id>/session/orchestration/

  • Compression: Older than 1 day

  • Retention Period: 7 days

  • Deletion: Archives older than 7 days


Solution Overview

The solution uses a lightweight Bash script that:

  1. Dynamically identifies SSM orchestration directories across instance IDs

  2. Compresses session folders older than one day

  3. Retains compressed archives for seven days

  4. Automatically deletes expired archives

  5. Runs daily using a cron job

This approach is simple, scalable, and does not require any changes to AWS-side configurations.


Implementation Steps

Step 1 — Create Cleanup & Compression Script

The script dynamically identifies orchestration directories, compresses older session folders, and deletes expired archives.


sudo tee /usr/local/bin/ssm_orch_retention.sh > /dev/null <<'EOF'
#!/bin/bash

# Retention and compression policy for SSM Orchestration logs
# Applies dynamically to any instance ID directory under /var/lib/amazon/ssm/

BASE_DIR="/var/lib/amazon/ssm"
RETENTION_DAYS=7

# Find all orchestration paths across all instance IDs
find "$BASE_DIR" -type d -path "*/session/orchestration" 2>/dev/null | while read -r ORCH_DIR; do
    [ ! -d "$ORCH_DIR" ] && continue

    echo "[INFO] Processing: $ORCH_DIR"

    # Compress directories older than 1 day (but newer than retention period)
    find "$ORCH_DIR" -mindepth 1 -maxdepth 1 -type d -mtime +1 -mtime -"$RETENTION_DAYS" ! -name "*.tar.gz" | while read -r d; do
        tar -czf "${d}.tar.gz" -C "$(dirname "$d")" "$(basename "$d")" && rm -rf "$d"
        echo "[COMPRESSED] $(basename "$d") -> ${d}.tar.gz"
    done

    # Delete compressed archives older than retention period
    find "$ORCH_DIR" -type f -name "*.tar.gz" -mtime +$RETENTION_DAYS -delete
done
EOF

                        


Step 2 — Manual Testing

Run the script manually and validate compressed and deleted logs.


sudo /usr/local/bin/ssm_orch_retention.sh

Expected Outcome

  • Session directories older than 1 day are compressed into .tar.gz

  • Compressed archives older than 7 days are removed automatically


Step 3 — Automate with Cron

Schedule the script to run daily at 2:00 AM using cron.


sudo bash -c 'echo "0 2 * * * /usr/local/bin/ssm_orch_retention.sh
>/var/log/ssm_orch_retention.log 2>&1" >> /etc/crontab'


Automation Benefits

  • No manual intervention required

  • Centralized execution logging

  • Consistent retention enforcement

Verification Checklist

Ensure compression, deletion, and cron execution are functioning as expected.


Key Notes and Considerations

  • The script dynamically detects instance ID directories

  • Designed for minimal operational impact

  • Helps prevent disk exhaustion caused by unmanaged orchestration logs

  • Retains a 7-day compressed audit trail for troubleshooting

  • Safe to deploy across multiple EC2 instances


Conclusion

Unmanaged SSM orchestration logs are one of those quiet issues that only surface when disk space runs out—usually at the worst possible time. By introducing a simple, automated retention and compression strategy, you turn log sprawl into a controlled, predictable process.



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