PREDICTIVE RESOURCE ALLOCATION IN AN EDGE COMPUTING NETWORK UTILIZING GEOLOCATION FOR ORCHESTRATION

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

receiving, from a mobile computing device, first location data for the mobile computing device at a first time;

receiving, from the mobile computing device, second location data for the mobile computing device at a second time;

based on the first location data and the second location data, determining a direction vector for the mobile computing device;

based on the direction vector:

identifying an edge node from a plurality of edge nodes corresponding to a predicted location of the mobile computing device; and

prior to the mobile computing device being in the predicted location, allocating computing resources for the computing service on the identified edge node.

2 . The computer-implemented method of claim 1 , wherein the mobile computing device is one of a smart phone, laptop, vehicle, drone, mobile computer, or a plane.

3 . The computer-implemented method of claim 1 , further comprising receiving a unique identifier for the mobile computing device and correlating the unique identifier with the received first location data and second location data 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 the 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).

6 . 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.

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

subsequent to allocating the computing resources, receiving, from the mobile computing device, the request for the computing service; and

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

8 . The computer-implemented method of claim 1 , wherein identifying the edge node from the plurality of edge nodes further comprises:

accessing service boundaries for the plurality of edge nodes;

comparing the direction vector to the service boundaries; and

based on the comparison of the direction vector to service boundaries, identifying the edge node.

9 . The computer-implemented method of claim 1 , wherein the computing service is database-based service and allocating the computing resources includes loading the database into memory of the identified edge node.

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

receiving, from an additional mobile computing device, third location data for the additional mobile computing device at the first time;

receiving, from the mobile computing device, fourth location data for the additional mobile computing device at the second time; and

wherein determining the direction vector is further based on the second location data and the third location data.

11 . A computer-implemented method for reducing latency in providing computing services, the method comprising:

receiving, from a mobile computing device, first location data for the mobile computing device at a first time;

based on the first location data, identifying a first edge node from a plurality of edge nodes;

receiving, from the mobile computing device, a request to perform a computing service;

performing, by the first edge node, at least a portion of the requested computing service;

receiving, from the mobile computing device, second location data for the mobile computing device at a second time;

based on the first location data and the second location data, determining a direction vector for the mobile computing device;

based on the direction vector:

identifying a second edge node from a plurality of edge nodes corresponding to a predicted location of the mobile computing device; and

allocating computing resources for the computing service on the second edge node so that the second edge node may continue performing the computing service.

12 . The computer implemented method of claim 11 , wherein the mobile computing device is one of a smart phone, laptop, vehicle, drone, mobile computer, or a plane.

13 . The computer implemented method of claim 11 , wherein the first edge node and the second edge node are located in different physical locations and include 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 computer implemented method of claim 11 , wherein allocating the computing resources on the second edge node 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.

15 . The computer implemented method of claim 11 , wherein identifying the first edge node from the plurality of edge nodes further comprises:

accessing service boundaries for the plurality of edge nodes;

comparing the first location to the service boundaries; and

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

16 . The computer implemented method of claim 11 , wherein identifying the second edge node from the plurality of edge nodes further comprises:

accessing service boundaries for the plurality of edge nodes;

comparing the direction vector to the service boundaries; and

based on the comparison of the direction vector to service boundaries, identifying the second edge node.

17 . A system for reducing latency in providing computing services, the system comprising:

at least one processor; and

memory storing instructions that, when executed by the at least one processor, causes the system to perform a plurality of operations comprising:

receiving, from a mobile computing device, first location data for the mobile computing device at a first time;

based on the first location data, identifying a first edge node from a plurality of edge nodes;

receiving, from the mobile computing device, a request to perform a computing service;

performing, by the first edge node, at least a portion of the requested computing service;

receiving, from the mobile computing device, second location data for the mobile computing device at a second time;

based on the first location data and the second location data, determining a direction vector for the mobile computing device;

based on the direction vector:

identifying a second edge node from a plurality of edge nodes corresponding to a predicted location of the mobile computing device; and

allocating computing resources for the computing service on the second edge node so that the second edge node may continue performing the computing service.

18 . The system of claim 17 , wherein the mobile computing device is one of a smart phone, laptop, vehicle, drone, or a plane.

19 . The system of claim 17 , wherein the first edge node and the second edge node are located in different physical locations and include at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).

20 . The system of claim 17 , wherein allocating the computing resources on the second edge node 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.