Czas trwania: 4 Hours
Cena netto: zł650,00
IBM Spectrum Virtualize Transparent Cloud Tiering supports creating connections to cloud service providers to store copies of volume data in private or public cloud storage – freeing up capacity on the system.
This module discusses how transparent cloud tiering features and functions, and how it can help administrators to create point-in-time snapshots of data on the system and then copied and stored on the cloud storage. Thus allowing administrators to restore snapshots from the cloud for disaster recovery purposes.
You will also authenticate Transparent Cloud Tiering on an IBM Spectrum Virtualize system to an OpenStack cloud server, create a cloud snapshot of a volume, and recover the volume snapshot from the cloud.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms
Upon completion of this course you should be able to:
Define hybrid cloud storage
Identify the attributes of transparent cloud tiering
Characterize the function of the cloud snapshot
Characterize the function of the cloud snapshot recovery
Assess the use cases of cloud storage
This course is for IBM personnel, IBM Business Partners, IT consultants and customers who are assessing and/or planning to deploy IBM System San Volume Controller Storage solutions and are interested in learning more on IBM Spectrum Virtualization.
IBM Spectrum Virtualize for the IBM SAN Volume Controller (SNV10G)
Snapshot to the Cloud
The exercise component implements Transparent Cloud Tiering using an IBM Spectrum Virtualize system to a OpenStack cloud server, creates a cloud snapshot of a volume, and recovers the volume snapshot from the cloud.Note: This simulation provides guided steps and hints on completing the exercise.
29 mar 2023
Web based Training