# US 12,181,998 B2 · Pattern-recognition enabled autonomous configuration optimization for data centers > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,998 B2 - Document ID: US12181998B2 - Status: Granted - Kind: B2 - Application number: 18/171,620 - Filed: 2023-02-20 - Published: 2023-06-29 - Granted: 2024-12-31 - Earliest claimed: 2020-02-26 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Kenny C. Gross; Sanjeev Raghavendrachar Sondur; Guang Chao Wang - Web page: https://patentgrep.com/patent/US12181998B2 ## CPC - G06F 9/5094: {where the allocation takes into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206)} - G06F 11/3433: {for load management (allocation of a server based on load conditions G06F9/505; load rebalancing G06F9/5083; redistributing the load in a network by a load balancer H04L67/1029)} - G05B 13/0265: {the criterion being a learning criterion} - G05B 13/042: {in which a parameter or coefficient is automatically adjusted to optimise the performance} - G06F 1/206: {comprising thermal management} - G06F 11/302: {where the computing system component is a software system} - G06F 11/3058: {Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations (thermal management in cooling arrangements of a computing system G06F1/206)} - G06F 11/3409: {for performance assessment} - G06F 11/3457: {Performance evaluation by simulation} - G06N 20/00: Machine learning - Y02D 10/00: Energy efficient computing, e.g. low power processors, power management or thermal management - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE ## Abstract A model-based approach to determining an optimal configuration for a data center may use an environmental chamber to characterize the performance of various data center configurations at different combinations of temperature and altitude. Telemetry data may be recorded from different configurations as they execute a stress workload at each temperature/altitude combination, and the telemetry data may be used to train a corresponding library of models. When a new data center is being configured, the temperature/altitude of the new data center may be used to select a pre-trained model from a similar temperature/altitude. Performance of the current configuration can be compared to the performance of the model, and if the model performs better, a new configuration based on the model may be used as an optimal configuration for the data center. ## Claims ### Claim 1 A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: identifying a data center configuration in an environmental chamber, wherein the environmental chamber is configured to adjustably control environmental characteristics around the data center configuration while in operation; executing a workload by the data center configuration while in the environmental chamber; while executing the workload, adjusting a temperature and a simulated altitude around the data center configuration; recording performance data from the data center configuration while executing the workload; and training a model using the performance data from the data center configuration, wherein the model is trained to receive requirements for a data center as an input and generate a recommended data center configuration as an output at the simulated altitude and temperature. ### Claim 2 (depends on 1) The non-transitory computer-readable medium according to claim 1, wherein the performance data is recorded at each of a plurality of increments of the temperature and the simulated altitude around the data center configuration. ### Claim 3 (depends on 2) The non-transitory computer-readable medium according to claim 2, wherein the plurality of increments of the temperature includes temperatures between approximately 15° C. and 35° C. ### Claim 4 (depends on 2) The non-transitory computer-readable medium according to claim 2, wherein the plurality of increments of the temperature are incremented at intervals of approximately 1° C. ### Claim 5 (depends on 2) The non-transitory computer-readable medium according to claim 2, wherein the plurality of increments of the simulated altitude includes altitudes between approximately sea level and 5000 feet. ### Claim 6 (depends on 2) The non-transitory computer-readable medium according to claim 2, wherein the plurality of increments of the simulated altitude are incremented at intervals of approximately 100 ft. ### Claim 7 (depends on 1) The non-transitory computer-readable medium according to claim 1, wherein the operations further comprise: placing a plurality of data center configurations in the environmental chamber; executing the workload by each of the plurality of data center configuration while in the environmental chamber; while executing the workload, adjusting the temperature and the simulated altitude around each of the plurality of data center configurations; recording performance data from each of the plurality of data center configurations while executing the workload; and training models corresponding to each of the plurality of data center configurations using the performance data from each of the plurality of data center configurations while executing the workload. ### Claim 8 (depends on 7) The non-transitory computer-readable medium according to claim 7, wherein the operations further comprise: receiving a first performance metric from a data center, wherein: the data center comprises a first configuration; and the first performance metric is generated as the data center executes the workload; receiving one or more environmental characteristics for the data center as the data center executes the workload; identifying a first model from the plurality of models that was trained using data from one of the plurality of data center configurations that was operating in a simulated environment having environmental characteristics that are similar to the one or more environmental characteristics of the data center; generating a second performance metric from the first model; comparing the second performance metric to the first performance metric; and determining a second configuration for the data center from the first model. ### Claim 9 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the one or more environmental characteristics comprise an ambient temperature surrounding the data center and an altitude at which the data center is installed. ### Claim 10 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein identifying the first model comprises: executing a nearest-neighbor algorithm to identify the environmental characteristics that are most similar to the one or more environmental characteristics. ### Claim 11 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the nearest-neighbor algorithm minimizes a difference between a temperature for the first model and a temperature for the data center, and minimizes a difference between a simulated altitude of the first model and an altitude of the data center. ### Claim 12 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the first configuration comprises a number and type of processors in the data center. ### Claim 13 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the first configuration comprises a number and type of hard disk drives in the data center. ### Claim 14 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the first configuration comprises a data cache size. ### Claim 15 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the data center comprises a cloud data center. ### Claim 16 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the operations further comprise: comparing the first performance metric to a Service Level Agreement (SLA); and determining that the first performance metric does not meet the SLA, wherein the first model is identified in response to determining that the first performance metric does not meet the SLA. ### Claim 17 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the operations further comprise: determining that the second performance metric exceeds the first performance metric; and providing the second configuration to be implanted by the data center. ### Claim 18 (depends on 8) The non-transitory computer-readable medium according to claim 8, wherein the first performance metric comprises a processor performance for each processor in the data center, and an I/O performance for each hard disk drive in the data center. ### Claim 19 A system comprising: one or more processors; and one or more memory devices comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: identifying a data center configuration in an environmental chamber, wherein the environmental chamber is configured to adjustably control environmental characteristics around the data center configuration while in operation; executing a workload by the data center configuration while in the environmental chamber; while executing the workload, adjusting a temperature and a simulated altitude around the data center configuration; recording performance data from the data center configuration while executing the workload; and training a model using the performance data from the data center configuration, wherein the model is trained to receive requirements for a data center as an input and generate a recommended data center configuration as an output at the simulated altitude and temperature. ### Claim 20 A method for autonomously determining an optimal configuration for data centers using a library of models pre-trained at various combinations of environmental characteristics, the method comprising: identifying a data center configuration in an environmental chamber, wherein the environmental chamber is configured to adjustably control environmental characteristics around the data center configuration while in operation; executing a workload by the data center configuration while in the environmental chamber; while executing the workload, adjusting a temperature and a simulated altitude around the data center configuration; recording performance data from the data center configuration while executing the workload; and training a model using the performance data from the data center configuration, wherein the model is trained to receive requirements for a data center as an input and generate a recommended data center configuration as an output at the simulated altitude and temperature.