EP-4741996-A1 - MACHINE LEARNING OF RELATIONSHIP BETWEEN AMBIENT AIR TEMPERATURE AND POWER CONSUMPTION

Abstract

Example devices and techniques are described. An example device includes one or more processors and one or more memories storing instructions. When executed, the instructions cause the one or more processors to determine a respective configured ambient temperature for each of a plurality of network devices. The instructions cause the one or more processors to determine a respective current traffic load on each of the plurality of network devices. The instructions cause the one or more processors to determine, for each of the plurality of network devices and based on the respective configured ambient temperatures and the respective current traffic loads, a respective estimated power usage value. The instructions cause the one or more processors to sum the respective estimated power usage values to generate an overall estimated power usage value and output a representation of the overall estimated power usage value.

Inventors

• KOMMULA, RAJA
• SUNKADA, Ganesh Byagoti Matad
• SRIDHAR, THAYUMANAVAN
• YAVATKAR, RAJENDRA SHIVARAM

Assignees

• Juniper Networks, Inc.

Dates

Publication Date

20260513

Application Date

20251030

Claims (15)

1. A computing device comprising: one or more processors; and one or more memories storing instructions, which, when executed by the one or more processors, cause the one or more processors to: determine a respective configured ambient temperature for each of a plurality of network devices; determine a respective current traffic load on each of the plurality of network devices; determine, for each of the plurality of network devices and based on the respective configured ambient temperatures and the respective current traffic loads, a respective estimated power usage value; generate an overall estimated power usage value based at least in part on the respective estimated power usage values; and output a representation of the overall estimated power usage value.
2. The computing device of claim 1, wherein a configured ambient temperature of a first network device of the plurality of network devices comprises a configured maximum operating temperature of the first network device.
3. The computing device of any of claims 1-2, wherein to determine the respective estimated power usage value, the instructions cause the computing device to provide, to one or more machine learning models, the respective configured ambient temperatures and the respective current traffic loads to obtain the respective estimated power usage value.
4. The computing device of claim 3, wherein the one or more machine learning models are trained using historical ambient temperature data, historical traffic load data, and historical power usage data.
5. The computing device of any of claims 1-4, wherein the instructions further cause the computing device to: determine, based on an air temperature, an estimated ambient temperature of a first network device of the plurality of network devices, the estimated ambient temperature comprising a programmable maximum operating temperature of the first network device; and output a representation of the estimated ambient temperature of the first network device.
6. The computing device of claim 5, wherein the air temperature comprises at least one of an inlet air temperature measured by a first temperature sensor located on or within the first network device, an average of a plurality of air temperatures measured by a plurality of temperature sensors located within the first network device, an inlet air temperature measured by a second temperature sensor located within a facility in which the first network device is located, or an external temperature measured by a third temperature sensor located outside the facility in which the first network device is located.
7. The computing device of any of claims 5-6, wherein the estimated ambient temperature of the network device comprises a recommended maximum operating temperature of the network device.
8. The computing device of any of claims 5-7, wherein to determine the estimated ambient temperature of the network device, the instructions cause the computing device to provide, to one or more machine learning models, at least one of the air temperature or a fan speed of the first network device to obtain the estimated ambient temperature of the network device.
9. The computing device of claim 8, wherein the one or more machine learning models are trained on at least two of historical air temperature data, historical fan speed data, or configured ambient temperatures for the plurality of network devices.
10. The computing device of any of claims 5-9, wherein the representation of the estimated ambient temperature of the first network device comprises at least one of a visual representation of a recommended maximum operating temperature to be displayed via a user interface or a command to the first network device to change a configured ambient temperature of the first network device to the estimated ambient temperature of the network device.
11. A method comprising: determining, by one or more processors, a respective configured ambient temperature for each of a plurality of network devices; determining, by the one or more processors, a respective current traffic load on each of the plurality of network devices; determining, by the one or more processors, for each of the plurality of network devices and based on the respective configured ambient temperatures and the respective current traffic loads, a respective estimated power usage value; generating, by the one or more processors, an overall estimated power usage value based at least in part on the respective estimated power usage values; and outputting, by the one or more processors and to an output device, a representation of the overall estimated power usage value.
12. The method of claim 11, wherein a configured ambient temperature of a first network device of the plurality of network devices comprises a configured maximum operating temperature of the first network device.
13. The method of any of claims 11-12, wherein determining the respective estimated power usage value comprises providing, to one or more machine learning models, the respective configured ambient temperatures and the respective current traffic loads to obtain the respective estimated power usage value.
14. The method of claim 13, wherein the one or more machine learning models are trained using historical ambient temperature data, historical traffic load data, and historical power usage data.
15. A computer-readable storage medium encoded with instructions for causing one or more programmable processors to perform the method recited by any of claims 11-14.

Description

This application claims the benefit of US Patent Application No. 19/341,916, filed 26 September 2025, and entitled "MACHINE LEARNING OF RELATIONSHIP BETWEEN AMBIENT AIR TEMPERATURE AND POWER CONSUMPTION," which claims the benefit of IN Provisional Patent Application No. 202441085971, filed 8 November 2024, and entitled "MACHINE LEARNING OF RELATIONSHIP BETWEEN AMBIENT AIR TEMPERATURE AND POWER CONSUMPTION," the entire content of each application is incorporated herein by reference. TECHNICAL FIELD This disclosure relates to computer network facilities that use power. BACKGROUND In a typical cloud data center environment, there is a large collection of interconnected servers that provide computing and/or storage capacity to run various applications. For example, a data center may comprise a facility that hosts applications and services for subscribers, e.g., customers of the data center. The data center may, for example, host all of the infrastructure equipment, such as networking and storage systems, redundant power supplies, and environmental controls. In a typical data center, clusters of storage servers and application servers (compute nodes) are interconnected via high-speed switch fabric provided by one or more tiers of physical network switches and routers. More sophisticated data centers provide infrastructure spread throughout the world with subscriber support equipment located in various physical hosting facilities. As data centers become larger, energy usage by the data centers increases. Some large data centers require a significant amount of power (e.g., around 100 megawatts), which is enough to power a large number of homes (e.g., around 80,000). Data centers may also run application workloads that are compute and data intensive, such as crypto mining and machine learning applications, that consume a significant amount of energy. As energy use has risen, customers of data centers and data center providers themselves have become more concerned about efficient use of power. SUMMARY Particular aspects are set out in the appended independent claims. Various optional embodiments are set out in the dependent claims. In general, techniques are described for power management of network devices. In particular techniques are described for determining recommended chassis ambient temperatures (e.g., recommended maximum operating temperatures) for network devices and for ambient temperature-based power estimation using machine learning. Network devices generally have a maximum operating temperature that can be set by a network administrator, which may sometimes be referred to as an ambient temperature. The maximum fan speed may be determined by this configured ambient temperature. As fan speed increases, the device's power consumption also rises. When temperatures are higher, the device generally consumes more power because the fans must run at higher speeds to keep the chassis temperature within the set limits (e.g., under the maximum operating temperature). Conventionally, an administrator may monitor the external weather temperature and adjust the network devices' ambient temperature accordingly. If the administrator forgets or neglects to configure the ambient temperature based on external conditions, a conventional network may waste power. This is particularly noticeable when the external temperature is significantly lower than a currently configured ambient temperature. Also, because the power consumption of network devices and the overall network is influenced by ambient temperature, network administrators often struggle to allocate the appropriate amount of power without knowing the power requirements associated with different ambient settings. Conventionally, network administrators may typically rely on external weather conditions to determine an appropriate ambient temperature for each network device in a network configuration. However, once the network administrator establishes such an ambient temperature value, the network administrator may remain uncertain about how this configuration will impact power consumption. This uncertainty can lead to either over-subscribing or under-subscribing power at the power grids, resulting in wasted energy and increased costs or, conversely, power shortages in conventional networks. The techniques of this disclosure may determine recommended ambient temperatures for network devices and/or may estimate power requirements in relation to current traffic load based on the ambient temperature(s). The techniques of the disclosure may therefore provide specific improvements to the computer-related field of computer network and data center power management that may have one or more practical applications. For example, such techniques may result in the saving of power over conventional data centers' power facilities by reducing ambient temperatures and thereby reducing fan speed of devices when cool air feeding a data center or other network facility is of a lower tempe