Predictive resource allocation in an edge computing network utilizing machine learning

1. A computer-implemented method for reducing latency in providing a computing service, the method comprising:

aggregating historical request data for a plurality of requests, wherein the aggregated historical request data includes at least the following data for a plurality of requests: a time of the request, a location of a device from which the request originated, and a type of computing service being requested;

training a machine learning model based on the aggregated historical request data;

generating, from the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location;

based on the generated predicted location, identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and

based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node.

2. The computer-implemented method of claim 1 , wherein the machine learning model is at least one of a decision tree, a random forest, a neural network, a continual learning model, or a deep learning model.

3. The computer-implemented method of claim 1 , further comprising assigning a unique identifier one or more mobile computing devices to allow for tracking of the mobile computing device.

4. The computer-implemented method of claim 3 , wherein the unique identifier is one of a cookie or a media access control (MAC) address.

5. The computer-implemented method of claim 1 , wherein allocating the computing resources comprises performing at least one of:

deploying a virtualized software;

deploying a virtualized instance;

deploying a virtualized machine;

deploying virtualized infrastructure;

deploying a virtualized container;

loading a database into memory of the identified edge node;

caching content for the computing service; or

allocating storage resources in memory of the identified edge node.

6. The computer-implemented method of claim 1 , wherein identified edge node includes at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).

7. The computer-implemented method of claim 1 , further comprising:

receiving, from a computing device, a request for the predicted service type at the predicted time; and

performing, by the identified edge node, the requested service with the allocated computing resources.

8. The computer-implemented method of claim 7 , wherein the computing device is one of a smart phone, laptop, vehicle, drone, a mobile computer, or a plane.

9. A system for reducing latency in providing a computing service, the system comprising:

a plurality of edge nodes having different physical locations;

a database storing historical request data for a plurality of requests;

at least one processor; and

memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to perform a set of operations comprising:

training a machine learning model based on the historical request data;

generating, from the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location;

based on the generated predicted location, identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and

based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node prior to the predicted time.

10. The system of claim 9 , wherein the historical request data includes at least the following data for a plurality of requests: a time of the request, a location of the device from where the request originated, and a type of service being requested.

11. The system of claim 9 , wherein the machine learning model is at least one of a decision tree, a random forest, a neural network, a continual learning model, or a deep learning model.

12. The system of claim 9 , wherein allocating the computing resources comprises performing at least one of:

deploying a virtualized software;

deploying a virtualized instance;

deploying a virtualized machine;

deploying virtualized infrastructure;

deploying a virtualized container;

loading a database into memory of the identified edge node;

caching content for the computing service; or

allocating storage resources in memory of the identified edge node.

13. The system of claim 9 , wherein identified edge node includes at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).

14. The system of claim 9 , wherein the operation further comprise:

receiving, from a mobile computing device, a request for the predicted service type at the predicted time; and

performing, by the identified edge node, the requested service with the allocated computing resources.

15. The system of claim 14 wherein the mobile computing device is one of a smart phone, laptop, vehicle, drone, a mobile computer, or a plane.

16. A computer-implemented method for reducing latency in providing a computing service, the method comprising:

receiving, from a plurality of computing devices, a plurality of real-time requests for services to be performed by one or more edge nodes of a plurality of edge nodes;

providing data representative of the plurality of real-time requests as an input into a trained machine learning model, wherein the trained machine learning model has been trained on historical request data;

generating, based on an output of the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location;

based on the generated predicted location, identifying an edge node, from the plurality of edge nodes, based on a physical location of the identified edge node; and

based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node prior to the predicted time.

17. The computer-implemented method of claim 16 , further comprising training the trained machine learning model based on the historical request data, wherein the historical request data includes at least the following data for a plurality of requests: a time of the request, a location of the device from where the request originated, and a type of service being requested.

18. The computer-implemented method of claim 16 , wherein identifying the edge node from the plurality of edge nodes comprises:

accessing service boundaries for the plurality of edge nodes;

comparing the predicted location to the service boundaries; and

based on the comparison of the predicted location to service boundaries, identifying the edge node.

19. The computer-implemented method of claim 16 , wherein allocating the computing resources comprises performing at least one of:

deploying a virtualized software;

deploying a virtualized instance;

deploying a virtualized machine;

deploying virtualized infrastructure;

deploying a virtualized container;

loading a database into memory of the identified edge node;

caching content for the computing service; or

allocating storage resources in memory of the identified edge node.

20. The computer-implemented method of claim 16 , wherein identified edge node includes at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).