System and method for recommending cost optimization options for a cloud resource

A system for recommending one or more cost optimization options for a cloud resource, the system comprising:

• an input/output interface to receive one or more information of a cloud resource, wherein the one or more information includes one or more pricing offers from one or more public cloud providers, performance metrics, activity logs, business criticality data, and billing data of the cloud resource;
• and
• a memory in communication with one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to:
• analyze the received one or more information of the cloud resource to identify a type of the cloud resource, a type of environment where the cloud resource is running, a usage pattern of the cloud resource, and a nature of business, wherein the cloud resource is classified as mission-critical cloud resource or non-mission-critical cloud resource based on a nature of software programs hosted thereon, and wherein the mission-critical cloud resource is assigned with elevated privileges including at least one of:
• use of high-service configurations comprising increased compute cores and a memory for virtual machines, access to premium service tiers, and enablement of always-on operational modes, wherein the type of environment is classified as a production environment or a non-production environment, wherein an intelligent analysis is performed on the cloud resource to determine an environment in which the cloud resource is operating, wherein the intelligent analysis evaluates current utilization of the cloud resource, and considers different cost models offered by cloud providers to generate a recommendation for an optimal target state configured to achieve maximum cost savings, wherein each usage pattern is a combination of one or more optimization levers applied together, including a sequence of application, wherein the recommendation is stored in a data store if there is an option to save cost;
• generate, one or more cost optimization options using a cost analysis engine by analyzing cloud resource information, wherein the one or more cost optimization options are stored in a cost optimization options store, and wherein the I/O interface reads the one or more cost optimization options from the cost optimization options store and display to a user;
• identify an optimization model from a predefined library based on the identified type of the cloud resource to determine the one or more cost optimization options for the cloud resource, wherein the identified optimization model considers cloud preferences and constraints, wherein the identified optimization model considers all possible cost-optimization levers based on the type of the cloud resource, wherein the identified optimization model downsizes the virtual machine and purchases reservations;
• identify one or more applicable optimization patterns of the cloud resource based on the identified optimization model, wherein each of the one or more applicable optimization patterns having one or more optimization levers, and each of the one or more optimization levers comprising one or more criteria, wherein the one or more criteria is different for business critical and non-mission critical applications;
• validate each of the one or more criteria based on the received one or more information of the cloud resource to select at least one of the one or more applicable optimization patterns, wherein the validation process continues until each of the one or more optimization levers in each of the one or more applicable optimization patterns are validated;
• determine at least one target state for the selected at least one of the one or more applicable optimization patterns of the cloud resource, wherein the target state includes a recommended configuration, service tier, and pricing model, wherein the recommended configuration specifies a number of CPUs, size of RAM and service version for the virtual machine;
• forecast a utilization cost of the determined at least one target state for the one or more applicable optimization patterns of the cloud resource;
• recommend, the one or more cost optimization options based on the forecasted utilization cost for the one or more applicable optimization patterns of the cloud resource;
• and
• control, cloud spend in response to, real-time visibility into utilization of provisioned cloud resources.

A non-transitory computer readable medium storing one or more instructions which when executed by one or more processors on a system, cause the one or more processors to perform method comprising steps of:

• receiving, via an input/output interface, one or more information of a cloud resource, wherein the one or more information includes one or more pricing offers from one or more public cloud providers, performance metrics, activity logs, business criticality data, and billing data of the cloud resource;
• analyzing, via one or more hardware processors, the received one or more information of the cloud resource to identify a type of the cloud resource, a type of environment where the cloud resource is running, a usage pattern of the cloud resource, and a nature of business, wherein the cloud resource is classified as mission-critical cloud resource or non-mission-critical cloud resource based on a nature of software programs hosted thereon, and wherein the mission-critical cloud resource is assigned with elevated privileges including at least one of:
• use of high-service configurations comprising increased compute cores and a memory for virtual machines, access to premium service tiers, and enablement of always-on operational modes, wherein the type of environment is classified as a production environment or a non-production environment, wherein an intelligent analysis is performed on the cloud resource to determine an environment in which the cloud resource is operating, wherein the intelligent analysis evaluates current utilization of the cloud resource, and considers different cost models offered by cloud providers to generate a recommendation for an optimal target state configured to achieve maximum cost savings, wherein each usage pattern is a combination of one or more optimization levers applied together, including a sequence of application, wherein the recommendation is stored in a data store if there is an option to save cost;
• generating, one or more cost optimization options using a cost analysis engine by analyzing cloud resource information, wherein the one or more cost optimization options are stored in a cost optimization options store, and wherein the I/O interface reads the one or more cost optimization options from the cost optimization options store and displays to a user;
• identifying, via one or more hardware processors, an optimization model from a predefined library based on the identified type of the cloud resource to determine the one or more cost optimization options for the cloud resource, wherein the identified optimization model considers cloud preferences and constraints, wherein the identified optimization model considers all possible cost-optimization levers based on the type of the cloud resource, wherein the identified optimization model downsizes the virtual machine and purchases reservations;
• identifying, via the one or more hardware processors, one or more applicable optimization patterns of the cloud resource based on the identified optimization model, wherein each of the one or more applicable optimization patterns having one or more optimization levers, and each of the one or more optimization levers comprising one or more criteria, wherein the one or more criteria is different for business critical and non-mission critical applications;
• validating, via the one or more hardware processors, each of the one or more criteria based on the received one or more information of the cloud resource to select at least one of the one or more applicable optimization patterns, wherein the validation process continues until each of the one or more optimization levers in each of the one or more applicable optimization patterns are validated;
• determining, via the one or more hardware processors, at least one target state for the selected at least one of the one or more applicable optimization patterns of the cloud resource, wherein the target state includes a recommended configuration, service tier, and pricing model, wherein the recommended configuration specifies a number of Computer Processing Unit (CPUs), size of RAM and service version for the virtual machine;
• forecasting, via the one or more hardware processors, a utilization cost of the determined at least one target state for the one or more applicable optimization patterns of the cloud resource;
• recommending, via the input/output interface, the one or more cost optimization options based on the forecasted utilization cost for the one or more applicable optimization patterns of the cloud resource;
• and
• controlling, cloud spend in response to, real-time visibility into utilization of provisioned cloud resources.