POWER OPTIMIZATION OF A COMPUTING SYSTEM
This disclosure describes techniques for improving and/or reducing the power consumption by a router or other computing system. For example, this disclosure describes determining, by a computing system, an expected scale of a network device relative to a maximum scale of the network device; and adjusting, by the computing system and based on a comparison of the expected scale to the maximum scale, power consumption of the network device.
1 . A method comprising:
determining, by a computing system, an expected scale of a network device, wherein the network device comprises a plurality of hardware components;
comparing, by the computing system, the expected scale to a maximum scale of the network device; and
adjusting, by the computing system and based on the comparison, power consumption of the network device, wherein adjusting power consumption includes offlining one or more of the hardware components of the network device.
2 . The method of claim 1 , wherein the hardware components include a plurality of CPU cores, and wherein adjusting power consumption of the network device further includes:
reducing a frequency at which at least one of the CPU cores are clocked.
3 . The method of claim 1 , wherein the hardware components include a plurality of CPU cores, and wherein offlining one or more the hardware components includes:
offlining one of the CPU cores in the network device.
4 . The method of claim 1 , wherein the hardware components include a plurality of memory modules, and wherein offlining one or more the hardware components includes:
offlining one or more of the memory modules in the network device.
5 . The method of claim 1 , further comprising:
determining, by the computing system, an updated expected scale of the network device; and
further adjusting, by the computing system and based on the updated expected scale, power consumption of the network device.
6 . The method of claim 5 , wherein further adjusting power consumption includes:
onlining one or more of the hardware components.
7 . The method of claim 1 , wherein the computing system is included within the network device.
8 . The method of claim 1 , wherein the network device is a router, and wherein determining the expected scale of the router includes:
determining information about convergence capabilities of the router.
9 . The method of claim 8 , wherein determining the expected scale includes:
applying a machine learning model to predict the expected scale based on at least one of a configuration associated with the router, specifications associated with the router, switching operations, CPU utilization, core utilization, or memory utilization.
10 . A system comprising a storage device; and
processing circuitry having access to the storage device and configured to:
determine an expected scale of a network device, wherein the network device comprises a plurality of hardware components,
compare the expected scale to a maximum scale of the network device, and
adjust, based on the comparison, power consumption of the network device, wherein to adjust power consumption, the processing circuitry is further configured to offline one or more of the hardware components of the network device.
11 . The system of claim 10 , wherein the hardware components include a plurality of CPU cores, and wherein to adjust power consumption of the network device, the processing circuitry is further configured to:
reduce a frequency at which at least one of the CPU cores are clocked.
12 . The system of claim 10 , wherein the hardware components include a plurality of CPU cores, and wherein to offline one or more the hardware components, the processing circuitry is further configured to:
offline one of the CPU cores in the network device.
13 . The system of claim 10 , wherein the hardware components include a plurality of memory modules, and wherein to offline one or more the hardware components, the processing circuitry is further configured to:
offline one or more of the memory modules in the network device.
14 . The system of claim 10 , wherein the processing circuitry is further configured to:
determine an updated expected scale of the network device; and
further adjust, based on the updated expected scale, power consumption of the network device.
15 . The system of claim 14 , wherein to further adjust power consumption, the processing circuitry is further configured to:
online one or more of the hardware components.
16 . The system of claim 10 ,
wherein the system is included within the network device.
17 . The system of claim 10 , wherein the network device is a router, and wherein to determine the expected scale, the processing circuitry is further configured to:
determine information about convergence capabilities of the router.
18 . The system of claim 17 , wherein to determine the expected scale, the processing circuitry is further configured to:
apply a machine learning model to predict the expected scale based on at least one of the router's configuration, construction, switching operations, CPU utilization, core utilization, or memory utilization.
19 . Non-transitory computer-readable storage media comprising instructions that, when executed, configure processing circuitry to:
determine an expected scale of a network device, wherein the network device comprises a plurality of hardware components;
compare the expected scale to a maximum scale of the network device; and
adjust, based on the comparison, power consumption of the network device, wherein to adjust power consumption, the processing circuitry is further configured to offline one or more of the hardware components of the network device.
20 . The computer-readable storage media of claim 19 , further comprising instructions that, when executed, further cause the processing circuitry to:
determine an updated expected scale of the network device; and
further adjust, based on the updated expected scale, power consumption of the network device.