On-premises to cloud workload migration through cyclic deployment and evaluation
A method and system for on-premises to cloud workload migration through cyclic deployment and evaluation. Existing processes for transferring on-premises workloads onto the public cloud are often painstakingly manual and laborious, in order to ensure proper workload interoperability between the different infrastructures. To address this existing dilemma in inter-infrastructure workload migration, the disclosed method and system employs a cyclic monitoring, deploying, and evaluating scheme to automate and implement optimal strategies for migrating on-premises workloads onto public and/or hybrid cloud computing environments.
1. A method for on-premises to cloud workload migration, comprising:
collecting, while a workload is deployed on an on-premises infrastructure, on-premises information pertinent to the workload;
generating a set of cloud model recommendations based on the on-premises information;
selecting a cloud model from the set of cloud model recommendations;
deploying, using the cloud model, the workload onto a testing cloud infrastructure;
tuning the cloud model until an optimal cloud model is obtained; and
migrating, using the optimal cloud model, the workload onto a public cloud infrastructure.
2. The method of claim 1 , wherein the on-premises information comprises a layer-architecture configuration outlining a set of logical computing layers associated with the workload.
3. The method of claim 2 , wherein the on-premises information comprises a tier-architecture configuration outlining a set of physical computing tiers associated with the workload, wherein each physical computing tier hosts at least a subset of the set of logical computing layers.
4. The method of claim 3 , wherein the on-premises information further comprises a resource requirements configuration outlining information technology (IT) resources allocated to the workload on each physical computing tier of the set of physical computing tiers thereof.
5. The method of claim 3 , wherein the on-premises information further comprises historical resource utilization metrics capturing a performance of each physical computing tier of the set of physical computing tiers associated with the workload.
6. The method of claim 3 , wherein the on-premises information further comprises a set of dependency mappings respectively outlining a set of dependencies relied upon to implement the workload.
7. The method of claim 1 , wherein the testing cloud infrastructure is one selected from a group consisting of a hybrid cloud infrastructure and the public cloud infrastructure.
8. The method of claim 1 , wherein selection of the cloud model from the set of cloud model recommendations considers at least one selected from a group consisting of cost, management overhead, and service level agreement (SLA) compliance.
9. The method of claim 1 , wherein the cloud model is one selected from a group consisting of an infrastructure as a service (IaaS), a platform as a service (PaaS), and a software as a service (SaaS).
10. The method of claim 1 , wherein tuning the cloud model until the optimal cloud model is obtained, comprises:
while a condition is unmet, performing an iterative process, comprising:
collecting, while the workload is deployed on the testing cloud infrastructure using the cloud model, cloud workload metrics assessing a first performance of the workload thereon;
making a determination confirming that the condition is unmet;
generating, based on the determination, a new set of cloud model recommendations;
selecting a new cloud model from the new set of cloud model recommendations; and
deploying, using the new cloud model, the workload onto the testing cloud infrastructure;
when the condition is met, terminating the iterative process; and
designating the new cloud model, following a termination of the iterative process, as the optimal cloud model,
wherein the condition comprises the cloud workload metrics at least matching on-premises workload metrics assessing a second performance of the workload while the workload had been deployed on the on-premises infrastructure.
11. A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor, enables the computer processor to:
collect, while a workload is deployed on an on-premises infrastructure, on-premises information pertinent to the workload;
generate a set of cloud model recommendations based on the on-premises information;
select a cloud model from the set of cloud model recommendations;
deploy, using the cloud model, the workload onto a testing cloud infrastructure;
tune the cloud model until an optimal cloud model is obtained; and
migrate, using the optimal cloud model, the workload onto a public cloud infrastructure.
12. The non-transitory CRM of claim 11 , wherein the on-premises information comprises a layer-architecture configuration outlining a set of logical computing layers associated with the workload.
13. The non-transitory CRM of claim 12 , wherein the on-premises information comprises a tier-architecture configuration outlining a set of physical computing tiers associated with the workload, wherein each physical computing tier hosts at least a subset of the set of logical computing layers.
14. The non-transitory CRM of claim 13 , wherein the on-premises information further comprises a resource requirements configuration outlining information technology (IT) resources allocated to the workload on each physical computing tier of the set of physical computing tiers thereof.
15. The non-transitory CRM of claim 13 , wherein the on-premises information further comprises historical resource utilization metrics capturing a performance of each physical computing tier of the set of physical computing tiers associated with the workload.
16. The non-transitory CRM of claim 13 , wherein the on-premises information further comprises a set of dependency mappings respectively outlining a set of dependencies relied upon to implement the workload.
17. The non-transitory CRM of claim 11 , wherein the testing cloud infrastructure is one selected from a group consisting of a hybrid cloud infrastructure and the public cloud infrastructure.
18. The non-transitory CRM of claim 11 , wherein selection of the cloud model from the set of cloud model recommendations considers at least one selected from a group consisting of cost, management overhead, and service level agreement (SLA) compliance.
19. The non-transitory CRM of claim 11 , wherein the cloud model is one selected from a group consisting of an infrastructure as a service (IaaS), a platform as a service (PaaS), and a software as a service (SaaS).
20. The non-transitory CRM of claim 11 , further comprising computer readable program code to tune the cloud model, which when executed by the computer processor, further enables the computer processor to:
while a condition is unmet, perform an iterative process, comprising:
collecting, while the workload is deployed on the testing cloud infrastructure using the cloud model, cloud workload metrics assessing a first performance of the workload thereon;
making a determination confirming that the condition is unmet;
generating, based on the determination, a new set of cloud model recommendations;
selecting a new cloud model from the new set of cloud model recommendations; and
deploying, using the new cloud model, the workload onto the testing cloud infrastructure;
when the condition is met, terminate the iterative process; and
designate the new cloud model, following a termination of the iterative process, as the optimal cloud model,
wherein the condition comprises the cloud workload metrics at least matching on-premises workload metrics assessing a second performance of the workload while the workload had been deployed on the on-premises infrastructure.