Multitenant cross dimensional cloud resource visualization and planning
A system for migrating a non-tenant-aware local application to a tenant-aware cloud application environment is disclosed to migrate individual modules of the application to instances of the cloud by grouping the modules via common characteristics in partition groups. By grouping modules together by partition group before migrating the modules to cloud instances, modules that share resources can be placed in closer logical proximity to one another in the cloud, modified, or deleted to optimize performance. The information from these modules is processed to create a visualization graph representing information on the cloud. The visualization graph is preferably multilayered so it can display information from different hierarchical layers of a cloud instance.
1. A method for configuring workloads, comprising:
identifying cloud resources of an application and a set of cloud resource characteristics for each of the identified cloud resources;
creating a grouping of the cloud resources in reference to a cloud resource characteristic;
assigning the created grouping of the cloud resources to a workload as a function of the cloud resource characteristic of the cloud resources group;
constructing a plurality of workload assignment maps to assign to each of the workloads in accordance with the characteristic of the cloud resources group;
generating a cloud visualization of at least one of the plurality of workload assignment maps.
2. The method of claim 1 , wherein the set of cloud resources is at least one of a software component, a software dependency, a hardware dependency, and an application environment context.
3. The method of claim 1 , wherein the characteristic of the cloud resources group is at least one of a hardware characteristic, a software characteristic, and a network characteristic.
4. The method of claim 1 , wherein the cloud visualization is generated as a function of a network connection between a cloud resource and a workload.
5. The method of claim 1 , wherein the step of creating a grouping of the cloud resources is agnostic with respect to a network connection between a cloud resource and a workload.
6. The method of claim 1 , further comprising ranking each one of plurality of workload assignment maps based on a set of rules.
7. The method of claim 6 , wherein the set of rules comprise a permutation desirability metric for a workload assignment map, a user cost metric, a user efficiency metric, a security metric and a scalability metric.
8. The method of claim 2 , wherein the application environment context further comprises at least one of cloud logic and cloud metadata.
9. A system for configuring workloads, comprising:
a scanning engine configured to identify cloud resources of a non-tenant-aware application and a set of cloud resource characteristics for each of the identified cloud resources;
a grouping engine configured to group the cloud resources in reference to a cloud resource characteristic;
a mapping engine configured to assign the created grouping of the cloud resources to a workload as a function of the cloud resource characteristic of the cloud resources group; and
a rendering engine that constructs a plurality of workload assignment maps to assign to each of the workloads in accordance with the characteristic of the cloud resources group;
wherein the scanning engine, the grouping engine, the mapping engine, and the rendering engine comprise stored program instructions embedded in a non-transitory computer readable storage medium, and
wherein the stored program instructions are executed by a computer processor to execute a function.
10. The system of claim 9 , wherein the set of cloud resources is at least one of a software component, a software dependency, a hardware dependency, and an application environment context.
11. The system of claim 9 , wherein the characteristic of the cloud resources group is at least one of a hardware characteristic, a software characteristic, and a network characteristic.
12. The system of claim 9 , wherein a network characteristic is generated as a function of a network connection between a cloud resource and a workload.
13. The system of claim 9 , the grouping of the cloud resources is agnostic with respect to a network connection between a cloud resource and a workload.
14. The system of claim 9 further comprising an analysis engine configured to rank each one of the plurality of workload assignment maps based on a set of rule; and
wherein the analysis engine comprises stored program instructions embedded in the non-transitory computer readable storage medium.
15. The system of claim 14 , wherein the set of rules comprise a permutation desirability metric for a workload assignment map, a user cost metric, a user efficiency metric, a security metric and a scalability metric.
16. The system of claim 9 , further comprising an application environment context; and
wherein the application environment context further comprises at least one of cloud logic and cloud metadata.