**Description**

For a DAG that looks like this:

```
A >> B
```

If A fails, B goes into "upstream_failed" state. If a user then marks A "success", B will remain in "upstream_failed" state. It will not automatically start running. This scenario often happens if the failure of A is dealt with outside of Airflow and the user does not want Airflow to run A again. But he usually expect Airflow to run B after A is marked "success".

**Use case / motivation**

After A is marked "success", its downstream tasks B should be cleared automatically and get ready to be scheduled. To avoid changing this behaviour completely, there can be a toggle button next to "Success" that lets the user decide if he wants Airflow to automatically clear downstream tasks that are in "upstream_failed" state.
airflow/www/views.py
"""
        An endpoint helping check the health status of the Airflow instance,
        including metadatabase and scheduler.
        """
        payload = {'metadatabase': {'status': 'unhealthy'}}
        latest_scheduler_heartbeat = None
        scheduler_status = 'unhealthy'
        payload['metadatabase'] = {'status': 'healthy'}
        try:
            scheduler_job = SchedulerJob.most_recent_job()
            if scheduler_job:
                latest_scheduler_heartbeat = scheduler_job.latest_heartbeat.isoformat()
                if scheduler_job.is_alive():
                    scheduler_status = 'healthy'
        except Exception:  
            payload['metadatabase']['status'] = 'unhealthy'
        payload['scheduler'] = {
            'status': scheduler_status,
            'latest_scheduler_heartbeat': latest_scheduler_heartbeat,
        }
        return wwwutils.json_response(payload)
    @expose('/home')
    @auth.has_access(
        [
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_WEBSITE),
        ]
    )  
    def index(self):