Three, Two, One ... Go!

I'm pleased to be able to let you know that training for the OpenSSO Early Access Release is now available at https://opensso.dev.java.net.

The training comprises five self-paced, downloadable labs that take you through a complex OpenSSO deployment. You deploy two Apache Tomcat servers, SSL-enable them, install a software load balancer, install OpenSSO into the environment, and configure OpenSSO for session failover. Then you install an example web server and an example application server, and install Policy Agent software to see how OpenSSO protects web sites and J2EE applications.

Go to the OpenSSO site and click Training on the blue bar on the left. Follow the links that take you to the Sun Learning Services Online Lab Community.



From there, you will be able to get the labs, at no cost. The only thing you need before you can access the labs is a login ID at My Sun.

After following the setup instructions, you'll have:

  • A PDF-format workbook that contains the step-by-instructions for doing the labs



  • A Solaris 10, Update 5 virtual machine preconfigured with all the software you need to do five labs. This virtual machine has some interesting capabilities:

    • Four whole root zones, so scenarios requiring multiple hosts can be easily and quickly configured

    • All the zones are fully encapsulated in ZFS file systems. A script that exploits ZFS features lets students roll the virtual machine forward or backwards, to do any lab in the workbook. For example, if you are interested only in configuring session failover, you could roll the machine forward to Lab 3 (the session failover lab).



      If you then changed your mind and decided you wanted to do the lab in which you run the OpenSSO configurator, you could roll the machine back to the start of Lab 2. And, if you were then to decide you wanted to do the labs from scratch, you could roll the machine back to the starting point for Lab 1.


      Don't want to do the labs at all - just need to build OpenSSO demos? Roll the machine forward to the start point for Lab 6.
      The idea behind this type of learning is flexibility: "just in time, just enough, just for me."

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