# US 2021/0240539 A1 · Determining and implementing a feasilbe resource optimization plan for public cloud consumption > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240539 A1 - Document ID: US20210240539A1 - Status: Published application - Kind: A1 - Application number: 16/779,358 - Filed: 2020-01-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: Hewlett Packard Enterprise Development LP - Applicants: Hewlett Packard Enterprise Development LP - Inventors: Prabhu Murthy; Jyoti Ranjan; Abhishek Kumar - Web page: https://patentgrep.com/patent/US20210240539A1 ## CPC - G06F 9/5077: {Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455)} - G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling} - G06F 8/60: Software deployment - G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)} - G06F 11/301: {where the computing system is a virtual computing platform, e.g. logically partitioned systems (virtual machines G06F9/45533; logical partitioning of resources G06F9/5077)} - G06F 11/3034: {where the computing system component is a storage system, e.g. DASD based or network based (digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097)} - G06F 11/3409: {for performance assessment} - G06F 11/3452: {Performance evaluation by statistical analysis} - G06F 11/3485: {for I/O devices} - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06F 11/1446: Point-in-time backing up or restoration of persistent data - G06F 2009/45591: {Monitoring or debugging support} - G06F 2201/84: Using snapshots, i.e. a logical point-in-time copy of the data - G06N 3/08: Learning methods - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment. ## Claims ### Claim 1 A computer-implemented method comprising: obtaining telemetry data of a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources, wherein the telemetry data is collected over a period of time; obtaining information regarding a set of constraints to be imposed on a resource optimization plan, including application-specific and service-specific preferences and constraints; identifying a plurality of indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time by applying a deep learning algorithm to the telemetry data; and determining a resource optimization plan that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the plurality of indicators of resource consumption and costs associated with the current deployment. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising responsive to approval of the resource optimization plan, implementing the resource optimization plan by: reconfiguring the existing service and the application in accordance with the resource optimization plan; and re-deploying the application. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of indicators of resource consumption include (i) statistics relating to compute utilization, (ii) information regarding volume snapshot creation and associated snapshot utilization data, (iii) a provisioning model used to create instances, (iv) a life-span of each launched application, (v) information regarding backup creation and associated backup utilization data, (vi) a user workload pattern, (vii) a data consumption pattern, (viii) a data usage pattern, and (ix) a cloud services consumption pattern. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein statistics relating to compute utilization include average, minimum and maximum compute utilization during the period of time. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the provisioning model is indicative of whether the instances are reserved instances or spot instances. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the data consumption pattern is measured in terms of Input/Output Operations Per Second (IOPS). ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the data usage pattern is indicative of hot data and cold data. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the cloud services consumption pattern includes information regarding storage consumption and relational database services. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein said obtaining telemetry data and said identifying a plurality of indicators of resource consumption are performed on a periodic basis. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising determining the costs associated with the current deployment, a current resource utilization, and an ideal cost by crawling the current deployment. ### Claim 11 A non-transitory machine readable medium storing instructions executable by a processing resource of a computer system, the non-transitory machine readable medium comprising instructions to: obtain telemetry data of a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources, wherein the telemetry data is collected over a period of time; obtain information regarding a set of constraints to be imposed on a resource optimization plan, including application-specific and service-specific preferences and constraints; identify a plurality of indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time by applying a deep learning algorithm to the telemetry data; and determine a resource optimization plan that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the plurality of indicators of resource consumption and costs associated with the current deployment. ### Claim 12 (depends on 11) The non-transitory machine readable medium of claim 11, further comprising instructions to: responsive to approval of the resource optimization plan, implement the resource optimization plan by: reconfiguring the existing service and the application in accordance with the resource optimization plan; and re-deploying the application. ### Claim 13 (depends on 11) The non-transitory machine readable medium of claim 11, wherein the plurality of indicators of resource consumption include (i) statistics relating to compute utilization, (ii) information regarding volume snapshot creation and associated snapshot utilization data, (iii) a provisioning model used to create instances, (iv) a life-span of each launched application, (v) information regarding backup creation and associated backup utilization data, (vi) a user workload pattern, (vii) a data consumption pattern, (viii) a data usage pattern, and (ix) a cloud services consumption pattern. ### Claim 14 (depends on 11) The non-transitory machine readable medium of claim 11, wherein statistics relating to compute utilization include average, minimum and maximum compute utilization during the period of time. ### Claim 15 (depends on 11) The non-transitory machine readable medium of claim 11, wherein the provisioning model is indicative of whether the instances are reserved instances or spot instances. ### Claim 16 (depends on 11) The non-transitory machine readable medium of claim 11, wherein the data consumption pattern is measured in terms of Input/Output Operations Per Second (IOPS). ### Claim 17 (depends on 11) The non-transitory machine readable medium of claim 11, wherein the data usage pattern is indicative of hot data and cold data. ### Claim 18 (depends on 11) The non-transitory machine readable medium of claim 11, wherein the cloud services consumption pattern includes information regarding storage consumption and relational database services. ### Claim 19 (depends on 11) The non-transitory machine readable medium of claim 11, wherein said obtaining telemetry data and said identifying a plurality of indicators of resource consumption are performed on a periodic basis. ### Claim 20 (depends on 11) The non-transitory machine readable medium of claim 11, further comprising determining the costs associated with the current deployment, a current resource utilization, and an ideal cost by crawling the current deployment.