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Target Architecture

 

Components

 

 

Cockpit

  • manage job configuration, optionally with a repository service
  • manage release procedure for job configuration to Master
  • accept job events and job history from Master
  • report job events to event queue
  • report job history to reporting database
  • run authentication and authorization service
  • run web server and web services
  • run JobScheduler Controller Web GUI
  • bundle a number of Masters and delegate commands to Master

Explantion of Drawing

Cockpit

  • JobScheduler Supervisor  distributes configuration to Master Cluster members
  • Supervisor accepts job events and job history and stores them persistently
  • JobScheduler Controller is the GUI for monitoring and taking action

Master Cluster

  • Primary and Backup JobScheduler Master are synchronized in a Cluster
  • Cluster members address job execution requests to Agent Clusters
  • Cluster members report job events and the job history to the Controller

Autonom. Agent Cluster

  • A number of Agents is grouped in a Cluster for:
    • fixed priority scheduling
    • round-robin scheduling
  • An Agent Cluster manages outages of Agents autonomously




 

 

 

 

Master

  • control the job plan (calendar), what to run, when and where
  • forward daily plan to Autonomous Agent Cluster
  • accept  task execution result, job history and log information from Agents
  • optionally operate in an active-passive cluster

 

Explantion of Drawing

Controller

  • The Controller presents the user interface (GUI)
  • The Controller makes use of web services provided by the Supervisor to retrieve status information

Web Services

  • Web Services enforce authentication and authorization
  • Web Services provide information on
    • job events
    • job history
    • Master/Agent status

Supervisor

  • Supervisor manages job release procedure for a repository (Git, subversion etc.) or file system
  • Supervisor  sends job control commands to Master
  • Supervisor accepts job events and job history and stores them persistently

 

 

Autonomous Agent

  • implement a fault-tolerant peer-to-peer network of Agents
  • accept daily plan from Master
  • available for active-passive and active-active clustering:
    • fixed priority scheduling
    • round-robin scheduling
  • execute job chains independently from Master availability
  • resolve more complex dependencies with Master, e.g. for checks of the job history or of external events from other machines
  • report job history and log information back to Master
  • run distributed recovery files for recovery purposes
  • allow access by a number of Master instances
  • provide resilience features for reconciliation after Master connection loss

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