Features
===============

- **Monitoring** 
  PyScaler is able to monitor remote hosts and store the performance 
  data for further analysis and business logic.

- **SSH monitoring**
  
  PyScaler can connect to remote hosts via SSH, execute scripts to gather 
  performance data and index its output.

- **JMX monitoring**
  
  PyScaler can connect to remote JVMs via JMX and gather performance data
  via the exposed mbeans.  

- **Interactive counter graphs**
  
  The performance data can be easily visualized using available graphs.
  There are different graphs per monitored target and counters that can be zoomed.

- **Control**

  PySCaler can be execute actions against its managed nodes. These action
  can be execute manually or triggered automatically when defined 
  performance thresholds are hit.

- **Remote command execution**
  
  It can execute command locally on specific nodes or on all the nodes 
  from a cluster.

- **Fabric scripts execution**
  
  It can execute Fabric scripts that can be used to execute local or 
  remote shell commands (normally or via sudo) and uploading/downloading
  files, as well as auxiliary functionality such as prompting the 
  running user for input, or aborting execution. 
  
- **Triggers**
  
  PyScaler can analyze available performance data and automatically trigger
  defined actions when a performance threshold is reached during a certain
  amount of time.

- **Provisioning**

  PyScaler can be used to provision new servers.

- **EC2 node deployment**
  
  PyScaler can launch new Amazon EC2 instances with defined parameters.

- **Operating system configuration**
  
  PyScaler can provision nodes using Puppet.

- **Deployment scaling**
  
  The previously explained features (monitoring, control and provisioning) 
  can be used together to automatically scale a cloud based cluster.
  PyScaler will monitor the cluster and when a defined performance threshold 
  is reached then the necessary actions to deploy a new cluster node will  
  be executed, thus scaling it.

- **Task based engine**

  PyScaler makes uses of Celery to launch all lengthy or periodic tasks in the backend.
  Celery can be used to handle retries and distribution of the tasks.
  
- **Restful urls and API**

  PyScaler urls are cleanly designed to be understood by human beings.
  Most available objects are exposed via a restful API and JSON

- **Ajax based interface**

  The usage of a task based backend along the jquery and json enables the 
  development of a fluid ajax based interface.
  
- **High availability**

  Each component of PyScaler is designed to be highly available.