# US 12,009,974 B1 · Self-optimizing networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,009,974 B1 - Document ID: US12009974B1 - Status: Granted - Kind: B1 - Application number: 18/144,024 - Filed: 2023-05-05 - Granted: 2024-06-11 - Earliest claimed: 2023-05-05 - Assignees: DISH Wireless L.L.C. - Applicants: DISH Wireless L.L.C. - Inventors: Praveen Kumar Reddy Mada; Vinayak Sharma; Hamza Nasir Khokhar - Web page: https://patentgrep.com/patent/US12009974B1 ## CPC - H04L 41/0816: the condition being an adaptation, e.g. in response to network events - H04L 41/0897: by horizontal or vertical scaling of resources, or by migrating entities, e.g. virtual resources or entities - H04L 41/0895: Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow elements - H04L 41/147: for predicting network behaviour - H04L 41/16: using machine learning or artificial intelligence - H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities - H04L 43/20: the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV - H04L 47/83: based on usage prediction - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-optimizing networks. In some implementations, a method for self-optimizing networks includes obtaining information indicating performance metrics of a first set of computing resources of a distributed system; generating information representing usage of a wireless network at a first point in time; providing the information to a machine learning model trained to predict network events at a time subsequent to the first point in time; determining that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system; transmitting a signal configured to adjust the distributed computing system to deploy the second set of computing resources. ## Claims ### Claim 1 A method for optimizing networks comprising: obtaining, from a distributed computing system servicing requests from user devices of a wireless network, information indicating performance metrics of a first set of computing resources of the distributed computing system; generating, based on the information indicating performance metrics of the first set of computing resources, information representing usage of the wireless network at a first point in time; providing the information representing usage of the wireless network to a machine-learning model trained to predict, based on usage data, network events at a time subsequent to the first point in time; determining, based on an output of the machine-learning model, that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system, the second set of computing resources being different from the first set of computing resources; and transmitting, responsive to determining that the at least one particular network event is addressable using the second set of computing resources, a signal configured to adjust the distributed computing system to deploy the second set of computing resources. ### Claim 2 (depends on 1) The method of claim 1, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to power on or power off. ### Claim 3 (depends on 1) The method of claim 1, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to generate a replicated instance of a virtual machine for processing requests of a first type. ### Claim 4 (depends on 3) The method of claim 3, wherein the output from the machine-learning model indicates an increase of the requests of the first type at a time subsequent to the first point in time. ### Claim 5 (depends on 1) The method of claim 1, wherein the information indicating performance metrics of the first set of computing resources comprises one or more performance logs of the first set of computing resources. ### Claim 6 (depends on 5) The method of claim 5, wherein the one or more performance logs include one or more values indicating processing unit capacity, processing unit usage, available bandwidth, or utilization of the first set of one or more computing resources. ### Claim 7 (depends on 1) The method of claim 1, wherein generating the information representing usage of the wireless network at the first point in time comprises: selecting a subset of values included in the information indicating performance metrics of the first set of computing resources to be included in the information representing usage of the wireless network at the first point in time. ### Claim 8 (depends on 1) The method of claim 1, wherein the particular network event includes a future failure of a first computation resource of the first set of one or more computing resources and the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a second computing resources of the first set of one or more computing resources to handle requests previously scheduled to be handled by the first computing resources. ### Claim 9 A non-transitory computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: obtaining, from a distributed computing system servicing requests from user devices of a wireless network, information indicating performance metrics of a first set of computing resources of the distributed computing system; generating, based on the information indicating performance metrics of the first set of computing resources, information representing usage of the wireless network at a first point in time; providing the information representing usage of the wireless network to a machine-learning model trained to predict, based on usage data, network events at a time subsequent to the first point in time; determining, based on an output of the machine-learning model, that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system, the second set of computing resources being different from the first set of computing resources; and transmitting, responsive to determining that the at least one particular network event is addressable using the second set of computing resources, a signal configured to adjust the distributed computing system to deploy the second set of computing resources. ### Claim 10 (depends on 9) The medium of claim 9, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to power on or power off. ### Claim 11 (depends on 9) The medium of claim 9, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to generate a replicated instance of a virtual machine for processing requests of a first type. ### Claim 12 (depends on 11) The medium of claim 11, wherein the output from the machine-learning model indicates an increase of the requests of the first type at a time subsequent to the first point in time. ### Claim 13 (depends on 9) The medium of claim 9, wherein the information indicating performance metrics of the first set of computing resources comprises one or more performance logs of the first set of computing resources. ### Claim 14 (depends on 13) The medium of claim 13, wherein the one or more performance logs include one or more values indicating processing unit capacity, processing unit usage, available bandwidth, or utilization of the first set of one or more computing resources. ### Claim 15 (depends on 9) The medium of claim 9, wherein generating the information representing usage of the wireless network at the first point in time comprises: selecting a subset of values included in the information indicating performance metrics of the first set of computing resources to be included in the information representing usage of the wireless network at the first point in time. ### Claim 16 (depends on 9) The medium of claim 9, wherein the particular network event includes a future failure of a first computation resource of the first set of one or more computing resources and the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a second computing resources of the first set of one or more computing resources to handle requests previously scheduled to be handled by the first computing resources. ### Claim 17 A system, comprising: one or more processors; and machine-readable media interoperably coupled with the one or more processors and storing one or more instructions that, when executed by the one or more processors, perform operations comprising: obtaining, from a distributed computing system servicing requests from user devices of a wireless network, information indicating performance metrics of a first set of computing resources of the distributed computing system; generating, based on the information indicating performance metrics of the first set of computing resources, information representing usage of the wireless network at a first point in time; providing the information representing usage of the wireless network to a machine-learning model trained to predict, based on usage data, network events at a time subsequent to the first point in time; determining, based on an output of the machine-learning model, that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system, the second set of computing resources being different from the first set of computing resources; and transmitting, responsive to determining that the at least one particular network event is addressable using the second set of computing resources, a signal configured to adjust the distributed computing system to deploy the second set of computing resources. ### Claim 18 (depends on 17) The system of claim 17, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to power on or power off. ### Claim 19 (depends on 17) The system of claim 17, wherein the signal configured to adjust the distributed computing system to deploy the second set of computing resources comprises instructions for a computing resources of the first set of one or more computing resources to generate a replicated instance of a virtual machine for processing requests of a first type. ### Claim 20 (depends on 19) The system of claim 19, wherein the output from the machine-learning model indicates an increase of the requests of the first type at a time subsequent to the first point in time.