System and method for managing computing devices
A system and method for easily managing a data center with multiple computing devices such as cryptocurrency miners from different manufactures is disclosed. A first computer includes a management application to manage the selected computing devices and periodically read and store status information from them into a database. Controls are presented to enable selection of one or more of the devices and to apply an operating mode, including manual, semi-automatic, automatic, and intelligent modes. Machine learning may be used to determine recommended settings for the selected set of computing devices.
1. A method for managing a plurality of computing devices, the method comprising:
periodically collecting status information from the plurality of devices;
providing a user interface comprising:
a first control to select one or more of the plurality of devices, and
a second control to select one of a plurality of operating modes, wherein the operating modes comprise:
a manual mode; and
an intelligent mode;
in response to one of the operating modes being selected, applying the selected operating mode to the selected devices; and
in response to the intelligent mode being selected, preventing manual modification of operational parameters and permitting modifications of non-operational parameters.
2. The method of claim 1 , wherein the operating modes further comprise:
a semi-automated mode; and
an automated mode.
3. The method of claim 1 , further comprising:
in response to a user selecting the manual mode option, presenting in the user interface controls for setting the following device attributes:
chip frequency,
chip voltage, and
fan speed.
4. The method of claim 2 , further comprising:
periodically storing performance data for the each of the computing devices,
analyzing the stored performance data to determine a recommended setting, and
in response to the semi-automated mode being selected, presenting the recommended setting and displaying a control to apply the recommended setting to the selected devices.
5. The method of claim 2 , further comprising:
periodically storing performance data for the each of the computing devices,
analyzing the stored performance data to determine a recommended setting, and
in response to the automated mode being selected, applying the recommended setting to the selected devices, and
monitoring the status information, and
in response to detecting a degradation in the selected devices:
recalculating the recommended setting, and
applying the recalculated recommended setting.
6. The method of claim 1 , further comprising:
collecting additional parameters,
using the additional parameters to dynamically change settings at different points in the day to increase performance.
7. The method of claim 6 , further comprising:
applying machine learning to the status information.
8. The method of claim 6 , wherein the additional parameters comprise temperature, humidity, dust levels, hash rate, and chip health.
9. A system for managing a data center with a plurality of networked computers, the system comprising:
a management application executable on one of the plurality of computers, wherein the management application comprises:
a first module that periodically reads and stores status information from the plurality of networked computers, and
a second module operable to provide a user interface, wherein the user interface comprises:
a first control to select one or more of the plurality of networked computers, and
a second control to select one of a plurality of operating modes, wherein the operating modes comprise:
a manual mode, and
an intelligent mode,
wherein in response to one of the operating modes being selected, the management application is configured to apply the selected operating mode to the selected networked computers; and
in response to the intelligent mode being selected, the management application is configured to prevent manual modification of operational parameters and permit modifications of non-operational parameters.
10. The system of claim 9 , wherein the operating modes further comprise:
a semi-automated mode; and
an automated mode.
11. The system of claim 9 , wherein the second module, in response to a user selecting the manual mode option, presents in the user interface controls for setting the following device attributes: chip frequency, chip voltage, and fan speed.
12. The system of claim 10 , wherein the first module:
periodically stores performance data for the each of the networked computers
analyzes the stored performance data to determine a recommended setting, and
in response to the semi-automated mode being selected:
presents the recommended setting; and
displays a control to apply the recommended setting to the selected networked computers.
13. The system of claim 10 , wherein the first module:
periodically stores and monitors performance data for the each of the networked computers;
analyzes the stored performance data to determine a recommended setting, and
in response to the automated mode being selected, and, in response to detecting a degradation in the selected networked computers:
recalculates the recommended setting, and
automatically applies the recalculated recommended setting.
14. The system of claim 10 , further comprising:
a machine learning module that based on stored data determines preferred settings for the networked computers based on current status data to optimize hash efficiency.
15. A method for managing a plurality of computing devices, the method comprising:
periodically collecting status information from the plurality of devices;
providing a user interface comprising:
a first control to select one or more of the plurality of devices, and
a second control to select one of a plurality of operating modes, wherein the operating modes comprise:
a manual mode; and
an intelligent mode; and
in response to the intelligent mode being selected, periodically selecting a preferred set of device settings by using a mixed integer linear programming to optimize operation of the plurality of computing devices.
16. The method of claim 15 ,
in response to the intelligent mode being selected, preventing manual modification of operational parameters and permitting modifications of non-operational parameters.
17. The method of claim 15 , wherein the status information comprises hash rate, power consumption, and temperature.
18. The method of claim 15 , wherein the status information further comprises measurements of dust concentration, and the method further comprises in response to the intelligent mode being selected and detecting higher dust concentrations:
a. increasing a fan speed; or
b. reducing operating voltage and frequency.
19. The method of claim 15 , wherein the status information further comprises measurements of humidity, and the method further comprises in response to the intelligent mode being selected and detecting humidity:
a. decreasing fan speed; or
b. increasing operating voltage and frequency.
20. The method of claim 15 , further comprising:
in response to the manual mode being selected, permitting entry of beyond safe operating range destructive settings for the plurality of selected devices.