# US 2024/0202041 A1 · Method, apparatus and system for automatically scaling dynamic computing resources based on predicted traffic patterns > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0202041 A1 - Document ID: US20240202041A1 - Status: Published application - Kind: A1 - Application number: 18/515,576 - Filed: 2023-11-21 - Published: 2024-06-20 - Earliest claimed: 2022-12-19 - Assignees: Foundation of Soongsil University-Industry Cooperation - Applicants: Foundation of Soongsil University-Industry Cooperation - Inventors: Young Han KIM; Tae Hoon KIM - Web page: https://patentgrep.com/patent/US20240202041A1 ## CPC - G06F 9/505: {considering the load} - H04L 41/147: for predicting network behaviour - G06F 9/5072: {Grid computing} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract An apparatus, method, and system for automatically scaling dynamic computing resources based on predicted traffic patterns are disclosed. A system for automatically scaling dynamic computing resource based on a predicted traffic pattern comprises a prediction API for predicting resource according to a traffic request for each cluster configured on a workload server using a pre-trained machine learning model; and a prediction-based autoscaler for calculating required resource by comparing available resource for each cluster with the predicted resource, selecting an optimal flavor according to the calculated required resource, and generating a template corresponding to the selected optimal flavor. ## Claims ### Claim 1 A system for automatically scaling dynamic computing resource based on a predicted traffic pattern comprising: a prediction API for predicting resource according to a traffic request for each cluster configured on a workload server using a pre-trained machine learning model; and a prediction-based autoscaler for calculating required resource by comparing available resource for each cluster with the predicted resource, selecting an optimal flavor according to the calculated required resource, and generating a template corresponding to the selected optimal flavor. ### Claim 2 (depends on 1) The system of claim 1, wherein the pre-trained machine learning model is a Bi-LSTM model based on a recurrent neural network. ### Claim 3 (depends on 1) The system of claim 1, wherein the traffic request and the available resource are received by calling a monitoring system, wherein the monitoring system monitors traffic requests flowing into the workload server by segmenting them for each cluster. ### Claim 4 (depends on 3) The system of claim 3, wherein information on a collected traffic request is transmitted to a traffic mesh when a trigger point is activated in the monitoring system and a resource utilization rate of a given cluster is greater than or equal to a preset value. ### Claim 5 (depends on 4) The system of claim 4, wherein the trigger point is SNMP_exporter, wherein the SNMP_exporter is initially in an off state, and when the resource utilization rate is greater than or equal to a preset first threshold, the SNMP_exporter is changed to an on state and the information on the traffic request is stored in a database of the monitoring system. ### Claim 6 (depends on 5) The system of claim 5, wherein if the resource utilization rate is greater than or equal to a second threshold greater than the first threshold, the information on the traffic request stored in the database is transmitted to a storage for training the machine learning model. ### Claim 7 (depends on 1) The system of claim 1, wherein the template is compatible with a currently operating cluster management program. ### Claim 8 An apparatus for automatically scaling dynamic computing resource based on a predicted traffic pattern comprising: a processor; and a memory connected to the processor, wherein the memory stores program instructions for performing operations comprising, predicting resource according to a traffic request for each cluster configured on a workload server using a pre-trained machine learning model, calculating required resource by comparing available resource for each cluster with the predicted resource, selecting an optimal flavor according to the calculated required resource, and generating a template corresponding to the selected optimal flavor. ### Claim 9 A method for automatically scaling dynamic computing resource based on a predicted traffic pattern in an apparatus including a processor and a memory comprising: predicting resource according to a traffic request for each cluster configured on a workload server using a pre-trained machine learning model; calculating required resource by comparing available resource for each cluster with the predicted resource; selecting an optimal flavor according to the calculated required resource; and generating a template corresponding to the selected optimal flavor. ### Claim 10 (depends on 9) The method of claim 9, wherein the pre-trained machine learning model is a Bi-LSTM model based on a recurrent neural network. ### Claim 11 (depends on 9) The method of claim 9, wherein the traffic request and the available resource are received by calling a monitoring system, wherein the monitoring system monitors traffic requests flowing into the workload server by segmenting them for each cluster. ### Claim 12 (depends on 11) The method of claim 11, wherein information on a collected traffic request is transmitted to a traffic mesh when a trigger point is activated in the monitoring system and a resource utilization rate of a given cluster is greater than or equal to a preset value. ### Claim 13 (depends on 12) The method of claim 12, wherein the trigger point is SNMP_exporter, wherein the SNMP_exporter is initially in an off state, and when the resource utilization rate is greater than or equal to a preset first threshold, the SNMP_exporter is changed to an on state and the information on the traffic request is stored in a database of the monitoring system. ### Claim 14 (depends on 13) The method of claim 13, wherein if the resource utilization rate is greater than or equal to a second threshold greater than the first threshold, the information on the traffic request stored in the database is transmitted to a storage for training the machine learning model. ### Claim 15 (depends on 9) A computer program stored in a computer-readable recording medium for performing the method of claim 9.