Method and system of geographic migration of workloads between private and public clouds

Abstract

A database contains available cloud environments to which a virtual image workload may be deployed. The database includes ratings for each available cloud option, such as cost, distance, reliability, which workloads that environment may handle. A table of attributes and weights is used to create a rating of the requested deployment. This rating determines where the image is deployed. The invention discloses techniques for gathering additional information from the user about the virtual image workload to be deployed. A mapping algorithm applies the attributes and weights to the gathered information to create a rating for the deployment. An algorithm is then used to determine to which available cloud environment the workload will be deployed.

We claim: 
     
         1 . A method for geographic migration of workloads for processing between available cloud environments comprising:
 identifying a workload for processing;   identifying one or more cloud environments in a cloud network that are available for process workloads;   assessing activity of the identified cloud environments in the cloud network that are available for processing workloads;   identifying a machine in a cloud environment for processing a workload; and   processing the identified workload at the identified machine in the cloud environment.   
     
     
         2 . The method as described in  claim 1  further comprising after said identifying a machine in a cloud environment for processing a workload, determining a geographic location of the identified machine for processing the workload and migrating the identified workload for processing to the geographic location of the identified machine. 
     
     
         3 . The method as described in  claim 1  further comprising creating a database of available cloud environments to which a virtual image workload may be deployed, the available clouds in the database being rated according to a set of parameters that can include cost, distance, reliability, which workloads that environment may handle. 
     
     
         4 . The method as described in  claim 3  wherein said assessing activity of the identified cloud environments in the cloud network that are available for processing workloads further comprises:
 calculating latency performances of machines at a cloud environment of the workload for processing; and 
 calculating latency performances between one or more cloud environments in the cloud network. 
 
     
     
         5 . The method as described in  claim 4  wherein said identifying a machine in a cloud environment for processing a workload further comprises:
 combining calculated latency performance information and the information of the identified workload for processing; and 
 generating an assessment of the performance of processing the workload at various available cloud environments. 
 
     
     
         6 . The method as described in  claim 5  further comprising determining whether to migrate the workload identified for processing to a cloud environment at a different geographic location or to process the identified workload at its current cloud environment location. 
     
     
         7 . The method as described in  claim 4  wherein said identifying a machine in a cloud environment for processing a workload further comprises:
 creating a table of attributes; 
 creating a rating of the workload to be processed; and 
 determining a processing location for the identified workload based on the created rating for the workload. 
 
     
     
         8 . The method as described in  claim 1  wherein said identifying a workload for processing further comprises gathering additional information from the user about the virtual image workload to be deployed. 
     
     
         9 . The method as described in  claim 7  wherein said determining a processing location for the identified workload based on the created rating for the workload further comprises creating a mapping algorithm which applies the attributes and weights to the input from information gathered from an identified workload to create a rating for placement of the identified workload for processing. 
     
     
         10 . The method as described in  claim 9  further comprising creating an algorithm to determine to which available cloud environment the workload will be deployed.