Browse Prior Art Database

Autonomic Cloud Template Adjustment

IP.com Disclosure Number: IPCOM000214915D
Publication Date: 2012-Feb-13
Document File: 2 page(s) / 45K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is the method to have a smart virtual appliance (VA) template in the cloud to be able to learn based on feedback from its deployed virtual machines (VMs). Thus, later resizes, moves, and adjustments to the VMs can be avoided.

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Autonomic Cloud Template Adjustment

This invention produces a new or changed virtual appliance (VA) template by analyzing empirical data collected from previous images that were in cloud production. Data analyzed includes artifacts produced by requests that images made in previous interactions in the cloud. This is a repeatable process based on historical image requests data analyzed and projected forward to a new cloud template.

    With the huge growth of the virtual world and cloud computing, the ability to have agile computing framework exists. Virtual machines (VM) and/or virtual workloads (VW) can be deployed for work, resized, moved as needed, deleted when done...etc.

    Usually an administrator sets up many templates (sometimes called virtual appliances), and users deploy virtual machines (sometime called workloads) which are live server instances constructed from the templates. An administrator sets up the template once, and then any number of instances of VMs can be deployed.

Now requests for resizing or moving of VMs may be generated by end users or IT/VM

management software. For example, more/faster CPU, more/faster memory, or more/faster disk, etc., may be requested for deployed and active VMs; i.e., VMs that are performing slower than expected, or VMs that generate a lot of data, etc., may request more resources. VM resizes and moves can take a lot of time to perform and may even require VMs to be reallocated to different physical hardware in the cloud. Further, consider many VMs within the cloud that are asking for more resources, CPU, memory, etc. These type of actions are slowing down performance of each image and the whole cloud can be affected..

    Invented is the method to have a smart VA template in the cloud to be able to learn based on feedback from its deployed VMs. Thus, later resizes, moves, and adjustments to the VMs can be avoided. This invention produces a new or changed VA template by analyzing empirical data collected from previous images that were in cloud production. Data analyzed includes artifacts produced by requests that images made in previous interactions in the cloud. This is a repeatable process based on hist...