System and method for monitoring a distributed computing network
The present disclosure is directed to methods and systems for monitoring nodes on a distributed computing network. A distributed computing system can monitor each node within a wireless network to identify when a node is disconnected from the network. The distributed computing system can dynamically perform tasks on behalf of the node until the node is reconnected to the wireless network. In some implementations, the distributed computing system monitors the utilization of the wireless network to identify when the wireless network has the capacity to perform a task.
1. A method for monitoring a distributed computing network, the method comprising:
receiving, from a device, a token that authorizes the device to connect to the distributed computing network, wherein the token indicates a type of distributed computing subscription associated with the device;
measuring a network utilization of the distributed computing network;
in response to the network utilization being below a threshold, sending a request to the device to perform a task based on the type of distributed computing subscription associated with the device;
receiving a notification that the device accepted the request; and
in response to receiving the notification, distributing the task to the device connected to the distributed computing network.
2. The method of claim 1 , further comprising:
selecting the device to perform the task based on a computing capability of the device.
3. The method of claim 1 , further comprising:
in response to the device performing the task, providing at least one reward to an account associated with the device, wherein the at least one reward is at least one of: a cryptocurrency, a share of stock, a coupon, a discount, an account credit, a statement credit, access to exclusive content, and an upgraded service package.
4. The method of claim 1 , further comprising:
detecting the device is disconnect from the distributed computing network; and
in response to detecting the device is disconnected, distributing the task to at least one second device connected to the distributed computing network, wherein the at least one second device performs the task while the device is disconnected from the distributed computing network.
5. The method of claim 1 , further comprising:
verifying the device is permitted to participate in the distributed computing network based on a comparison between permissions associated with the token and permissions stored in a ledger of the distributed computing network.
6. The method of claim 1 , wherein the network utilization is analyzed by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with past network utilization metrics.
7. The method of claim 1 , further comprising:
in response to the network utilization reaching the threshold while the device is performing the task, ceasing to distribute the task to the device.
8. A system comprising:
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform a process for monitoring a distributed computing network, the process comprising:
receiving, from a device, a token that authorizes the device to connect to the distributed computing network, wherein the token indicates a type of distributed computing subscription associated with the device;
measuring a network utilization of the distributed computing network;
in response to the network utilization being below a threshold, sending a request to the device to perform a task based on the type of distributed computing subscription associated with the device;
receiving a notification that the device accepted the request; and
in response to receiving the notification, distributing the task to the device connected to the distributed computing network.
9. The system according to claim 8 , wherein the process further comprises:
selecting the device to perform the task based on a computing capability of the device.
10. The system according to claim 8 , wherein the process further comprises:
in response to the device performing the task, providing at least one reward to an account associated with the device, wherein the at least one reward is at least one of: a cryptocurrency, a share of stock, a coupon, a discount, an account credit, a statement credit, access to exclusive content, and an upgraded service package.
11. The system according to claim 8 , wherein the process further comprises:
detecting the device is disconnect from the distributed computing network; and
in response to detecting the device is disconnected, distributing the task to at least one second device connected to the distributed computing network, wherein the at least one second device performs the task while the device is disconnected from the distributed computing network.
12. The system according to claim 8 , wherein the process further comprises:
verifying the device is permitted to participate in the distributed computing network based on a comparison between permissions associated with the token and permissions stored in a ledger of the distributed computing network.
13. The system according to claim 8 , wherein the network utilization is analyzed by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with past network utilization metrics.
14. The system according to claim 8 , wherein the process further comprises:
in response to the network utilization reaching the threshold while the device is performing the task, ceasing to distribute the task to the device.
15. A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for monitoring a distributed computing network, the operations comprising:
receiving, from a device, a token that authorizes the device to connect to the distributed computing network, wherein the token indicates a type of distributed computing subscription associated with the device;
measuring a network utilization of the distributed computing network;
in response to the network utilization being below a threshold, sending a request to the device to perform a task based on the type of distributed computing subscription associated with the device;
receiving a notification that the device accepted the request; and
in response to receiving the notification, distributing the task to the device connected to the distributed computing network.
16. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise:
selecting the device to perform the task based on a computing capability of the device.
17. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise:
in response to the device performing the task, providing at least one reward to an account associated with the device, wherein the at least one reward is at least one of: a cryptocurrency, a share of stock, a coupon, a discount, an account credit, a statement credit, access to exclusive content, and an upgraded service package.
18. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise:
detecting the device is disconnect from the distributed computing network; and
in response to detecting the device is disconnected, distributing the task to at least one second device connected to the distributed computing network, wherein the at least one second device performs the task while the device is disconnected from the distributed computing network.
19. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise:
verifying the device is permitted to participate in the distributed computing network based on a comparison between permissions associated with the token and permissions stored in a ledger of the distributed computing network.
20. The non-transitory computer-readable medium of claim 15 , wherein the network utilization is analyzed by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with past network utilization metrics.