The Growing Importance of Automated Failover and Self-Healing
As cloud and distributed systems have proliferated, resilience engineering has become crucial. Studies by Google and AWS on hardware failure rates reinforce that failures are commonplace:
- Avg server faces 5% chance of failure per year
- 100,000 server cluster sees ~50 server failures daily
At hyperscale, automated recovery processes are required to achieve high reliability. Various analyses have found that 70-80% of Kubernetes clusters implement auto-scaling and self-healing capabilities.
The ability to automatically restart crashed services via policy engines, cluster autoscalers and AIops platforms has become table stakes.
Trends in AIops Automating Failure Response
AIops refers to AIOps platforms applying analytics and ML to IT Operations tasks like incident remediation. They ingest metrics, logs and traces to detect anomalies and drive auto-remediation.
Leading examples include Splunk, Datadog, Dynatrace, and AWS CloudWatch. Adoption has grown over 30% CAGR as complex hybrid cloud infrastructure proliferates.
| Year | AIops Market Size | Growth % YoY |
| 2019 | $2.55B | 34% |
| 2020 | $4.25B | 38% |
| 2021 | $6.64B | 32% |
AIops removes the need for manual configuration of auto restart rules. The system listens to infrastructure signals and automatically remediates incidents per defined policy…
Why Auto Restart Services
The main benefits of automatically restarting failed services include:
- Minimizes downtime when crashes happen
- Avoids manual intervention to get services back up
- Provides breathing room to investigate root cause
- Reduces revenue impact from outages – avg cost $100K/hour
- Improves system resilience, uptime and availability
For example, if your MySQL database goes down at 3 AM, an auto restart script can detect the failure and immediately attempt to restart it. This brings the service back online much faster than if you had to manually intervene.
Ways to Auto Restart Services
1. Cron Jobs
A simple bash script that checks if a service is running, and restarts it if not, can be executed via cron. For example, this script could be run every 5 minutes by cron.
Pros:
- Easy to set up
- Lightweight
- Great for simple use cases
Cons:
- Only runs at scheduled intervals
- Doesn‘t continuously monitor services
- Manual effort to build and maintain
2. Systemd Service Restart
Systemd has built-in support for restarting failed services. You can configure the Restart property in the unit file.
For example:
[Service] Restart=always
Pros:
- Simple configuration at the OS level
- Enabled for all systemd services
- Automatic restarts without custom scripts
Cons:
- Requires systemd instead of SysV init
- Not as flexible as monitoring utilities
3. Process Monitoring Tools
Tools like Monit, God, and Supervisor are designed specifically for monitoring and controlling processes. They continuously watch services and can restart them when failures occur.
For example, Monit can be configured with:
check process mysql with pidfile /var/run/mysqld/mysqld.pid
start program = "/etc/init.d/mysql start"
stop program = "/etc/init.d/mysql stop"
if failed host 127.0.0.1 port 3306 then restart
Pros:
- Advanced monitoring and process management
- Highly configurable rules
- Can send alerts and notifications
- Option for gradual restarts
Cons:
- Increased complexity to set up
- Yet another system to monitor
- Manual effort to build and tune
| Tool | Restart Policies | Notifications | Supported Languages |
| Monit | Yes | Email, SMS | Any |
| God | Yes | Email, SMS | Ruby, Node.js |
| Supervisord | Yes | Std Out | Any |
Which method you choose depends on your needs, environment and technical comfort level. For many services, a simple cron script provides enough protection.
The Growth of Auto-Scaling and Self-Healing
Beyond just restarting failed processes, many organizations are now automating complete scale-in and scale-out responses using auto-scaling groups. These self-healing mechanisms automatically add or remove capacity to maintain desired performance levels.
Advantages include:
- Burstable scalability during traffic spikes
- Replace failed instances without manual intervention
- Distribute load evenly across resource pool
All major cloud providers (AWS, Azure, Google Cloud) provide auto-scaling capabilities out of the box. Kubernetes also launches and terminates pods based on metrics like CPU usage exceeding defined thresholds.
Third party tools like HPA, Cluster Autoscaler and MetricFire further enhance automation around scaling cluster resources in response to load and health data.
Example Auto Restart Scripts
Let‘s look at some real examples of auto restart scripts…
Restart Nginx
This script checks if the Nginx process is running, and starts Nginx if not. It would be executed via cron every 5 minutes.
#!/bin/bashsystemctl status nginx | grep ‘active (running)‘ >/dev/null
if [ $? -ne 0 ]; then systemctl start nginx
fi
Restart MySQL
This script pings the MySQL port to check connectivity. If it fails, MySQL is restarted via the system init script.
#!/bin/bashmysqladmin ping > /dev/null 2>&1
if [ $? -ne 0 ]; then service mysql restart
fi
Restart Apache Tomcat Java Web App
This script uses the Tomcat Manager status API to check if a Java web app is running, recycling the app if no response received.
#!/bin/bashurl=http://localhost:8080/manager/text/list
status=$(curl -s $url)
if [ $? -ne 0 ]; then
curl -s http://localhost:8080/manager/text/reload?path=/app fi
Restart Node.js Application
This JavaScript script imports the Process Manager module and registers event handler functions to restart on failures.
const pm2 = require(‘pm2‘);// Start app
pm2.start({ script : ‘app.js‘
})// Restart app on crash pm2.launchBus(function(err, bus) { bus.on(‘process:event‘, function(event) { if (event.event == ‘exit‘) { console.log(‘App crashed - restarting‘);
pm2.restart(‘app‘); } }); });
For additional examples and tutorials on auto restarting common services like Apache, Redis and more, check out our in-depth guide here.
Architectural Best Practices for High Reliability
Beyond just scripts to restart failed processes, scaling mission critical systems requires broader architectural principles:
- Active-active redundancy and failover capabilities
- Decoupled microservices allowing independent scaling/healing
- Circuit breakers prevent cascading failures
- Rate limiting protects downstream dependencies
- Chaos game days to proactively test system behaviour
Companies like Netflix, Amazon and Google embrace chaos engineering and actively inject failures into production readiness testing. This allows them to experience complex outages and learn how to handle them safely.
Configuration Management Tools
Rather than hand coding failover scripts, modern systems leverage configuration management utilities like:
- Kubernetes – define self-healing behavior in pod specs
- CloudFormation – template auto restaring infrastructure as code
- Terraform – provision self-healing infrastructure modules
- Ansible – auto restarts built into playbooks
These tools allow teams to declaratively define the desired steady state. The orchestrator handles day 2 operations to maintain that desired state through restaring, rescheduling and reprovisioning modules.
The AIops Advantage
AIops platforms further optimize failure handling by:
- Applying ML to histories of alerts and metrics data
- Detecting anomalies and emerging failures
- Diagnosing root cause through topology mapping
- Automating remediation procedures
- Feeding learnings back to models
For example, Dynatrace auto-baselines key application and infrastructure metrics. When early warning thresholds are exceeded, its topology mapping identifies impacted components to drive targeted auto-remediation.
This shifts organizations from reactive failure recovery, to predictive auto-prevention capabilities.
Business Benefits
What benefits do teams actually see from self-healing systems in production?
- Uptime improvements from 99.9% to 99.99% or higher
- Faster MTTR with automated remediation procedures
- Reduced human effort from manual alert response
- Lower operational costs from process automation
Stripe published about leveraging auto-scaling and Chaos Monkey testing to improve reliability. They were able to sustain 100% uptime even through unexpected outages.
Airbnb utilizes automated policy engines to restart crashed services. This has helped them drive both higher availability and developer efficiency.
Conclusion
Automatically restarting failed services is a critical redundancy that can dramatically improve reliability and uptime. This guide explored common techniques like cron scripts, systemd configs and advanced process monitors to achieve auto restarts.
We also discussed modern advances like chaos engineering, infrastructure as code tools, and AIops platforms that are automating and enhancing resiliency capabilities.
While the examples focused on Linux, the concepts apply equally to other operating systems. Implementing even basic auto restart capabilities can save you from many headaches down the road!
