System for allocating task processing between an IoT device and an edge device
Methods and systems are disclosed for allocating tasks between apparatus in an IoT system in a manner to generally minimize the total amount of time to execute the tasks. At least one embodiment includes a computer-implemented method for allocating task processing between an internet of things (IoT) device and an edge device. The computer-implemented method includes collecting data from one or more sensors to execute a task having data size Xt; predicting a space complexity data size Xc for the task based on data size Xt, and allocating data for processing between the IoT device and edge device as a function of Xc. In at least one embodiment, the space complexity data size Xc is determined by applying Xt to the input of a long short-term memory neural network.
1. A computer-implemented method for allocating task processing between an Internet of Things (IoT) device and an edge device, comprising:
collecting data from one or more sensors to execute a task having data size Xt;
predicting a space complexity data size Xc for the task based on the data size Xt;
allocating data for processing between the IoT device and edge device as a function of Xc;
determining a time Tprocess for processing data of size K*Xc at the edge device;
determining a time Tlocal for processing data of size [(1−K)*Xc)] at the IoT device;
minimizing Ttotal as a function of K, where Ttotal=Tprocess+Tlocal;
communicating data having data size Kopt*Xc from the IoT device to the edge device for execution at the edge device, where Kopt is a value of K selected to minimize Ttotal; and
executing data having data size [(1−K)*Xc)] at the IoT device.
2. The computer-implemented method of claim 1 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
3. The computer-implemented method of claim 1 , further comprising:
determining a resource availability A corresponding to availability of resources at the IoT device;
determining a resource availability B corresponding to availability of the resources at the edge device; and
using resource availabilities A and B in the minimization of Ttotal.
4. The computer-implemented method of claim 1 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
5. The computer-implemented method of claim 1 , further comprising:
receiving a first set of processing results from the edge device at the IoT device;
executing data having data size [(1−K)*Xc)] at the IoT device to produce a second set of processing results; and
aggregating the first and second set of processing results to generate a composite set of processing results.
6. The computer-implemented method of claim 5 , further comprising:
using the composite set of processing results to execute a task at the IoT device.
7. A system comprising:
one or more information handling systems, wherein the one or more information handling systems include:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus;
wherein the computer program code included in one or more of the information handling systems is executable by the processor of the information handling system so that the information handling system, alone or in combination with other information handling systems, executes operations comprising:
collecting data from one or more sensors in an IoT system to execute a task having data size Xt;
predicting a space complexity data size Xc for the task based on the data size Xt;
allocating data for processing between an IoT device and an edge device as a function of Xc;
determining a time Tprocess for processing data of size K*Xc at the edge device;
determining a time Tlocal for processing data of size [(1−K)*Xc)] at the IoT device;
minimizing Ttotal as a function of K, where Ttotal=Tprocess+Tlocal;
communicating data having data size Kopt*Xc from the IoT device to the edge device for execution at the edge device, where Kopt is a value of K selected to minimize Ttotal; and
executing data having data size [(1−K)*Xc)] at the IoT device.
8. The system of claim 7 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
9. The system of claim 7 , wherein the instructions are further configured for:
determining a resource availability A corresponding to availability of resources at the IoT device;
determining a resource availability B corresponding to availability of resources at the edge device; and
using resource availabilities A and B in the minimization of Ttotal.
10. The system of claim 7 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
11. The system of claim 7 , wherein the instructions are further configured for:
receiving a first set of processing results from the edge device at the IoT device;
executing data having data size [(1−K)*Xc)] at the IoT device to produce a second set of processing results; and
aggregating the first and second set of processing results to generate a composite set of processing results.
12. The system of claim 11 , wherein the instructions are further configured for:
using the composite set of processing results to execute a task at the IoT device.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
collecting data from one or more sensors in an IoT system to execute a task having data size Xt;
predicting a space complexity data size Xc for the task based on the data size Xt;
allocating data for processing between an IoT device and an edge device as a function of Xc;
determining a time Tprocess for processing data of size K*Xc at the edge device;
determining a time Tlocal for processing data of size [(1−K)*Xc)] at the IoT device;
minimizing Ttotal as a function of K, where Ttotal=Tprocess+Tlocal;
communicating data having data size Kopt*Xc from the IoT device to the edge device for execution at the edge device, where Kopt is a value of K selected to minimize Ttotal; and
executing data having data size [(1−K)*Xc)] at the IoT device.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
15. The non-transitory, computer-readable storage medium of claim 13 , wherein the instructions are further configured for:
determining a resource availability A corresponding to availability of resources at the IoT device;
determining a resource availability B corresponding to availability of the resources at the edge device; and
using resource availabilities A and B in the minimization of Ttotal.
16. The non-transitory, computer-readable storage medium of claim 13 , wherein
the space complexity data size Xc is determined by applying the data size Xt to an input of a trained long short-term memory neural network.
17. The non-transitory, computer-readable storage medium of claim 13 , wherein the instructions are further configured for:
receiving a first set of processing results from the edge device at the IoT device;
executing data having data size [(1−K)*Xc)] at the IoT device to produce a second set of processing results; and
aggregating the first and second set of processing results to generate a composite set of processing results.
18. The non-transitory, computer-readable storage medium of claim 17 , wherein the instructions are further configured for:
using the composite set of processing results to execute a task at the IoT device.