IP Library › Granted Patent US 12,200,041
Granted Patent B2
US 12,200,041 · App. 18/200,282 · Granted Jan 14, 2025

Application computation offloading for mobile edge computing

Inventors: Dario Sabella (Gassino, IT); Miltiadis Filippou (Munich, DE); Kilian Roth (Munich, DE); Ingolf Karls (Feldkirchen, DE); Yang Yang (Santa Clara, CA); Jing Zhu (Portland, OR)
Assignee: Intel Corporation
H04L67/04G06F9/5027G06F9/505G06F9/5072H04L47/762H04L47/803H04L67/10H04W28/0205H04W52/0264G06F2209/509Y02D30/70
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Quick Facts
Patent No.
US 12,200,041
App. No.
18/200,282
Granted
Jan 14, 2025
Kind
B2
Abstract

Systems, apparatuses, methods, and computer-readable media, are provided for offloading computationally intensive tasks from one computer device to another computer device taking into account, inter alia, energy consumption and latency budgets for both computation and communication. Embodiments may also exploit multiple radio access technologies (RATs) in order to find opportunities to offload computational tasks by taking into account, for example, network/RAT functionalities, processing, offloading coding/encoding mechanisms, and/or differentiating traffic between different RATs. Other embodiments may be described and/or claimed.

Claims (47)

1. A system configured to offload computational workloads from a user equipment (UE) in multi-access edge computing (MEC) network, the system comprising:

memory; and

processing circuitry coupled to the memory, the processing circuitry configured to:

identify an application to be utilized by the UE;

identify network characteristics of a radio access technology (RAT) accessible by the UE;

select the RAT for computational offloading of the application based on the network characteristics; and

transmit a computational offloading request via the RAT to a node associated with the RAT for execution of the application.

2. The system of claim 1 , wherein the RAT is local wireless fidelity and the node is a wireless fidelity access point.

3. The system of claim 1 , wherein the RAT is a direct wireless connection between the UE and a user computing device.

4. The system of claim 1 , wherein the processing circuitry is further configured to:

calculate an energy consumption value for a transmission of a data volume metric via the RAT using a radio of the UE; and

select the RAT in part based on the energy consumption value.

5. The system of claim 1 , wherein the processing circuitry is further configured to determine a link quality metric for a communication channel between the UE and a base station of the MEC network, wherein the RAT is selected in part based on the link quality metric.

6. The system of claim 5 , wherein the base station is an evolved NodeB (eNB) base station.

7. The system of claim 1 , wherein the MEC network includes a fifth generation (5G) new radio technology.

8. The system of claim 1 , wherein the processing circuitry is further configured to determine a battery state of the UE, wherein the RAT is selected in part based on the battery state of the UE.

9. The system of claim 1 , wherein the processing circuitry is further configured to determine a frequency band for the RAT, wherein the RAT is selected based on the determined frequency band.

10. A method for offloading computational workloads from a user equipment (UE) in multi-access edge computing (MEC) network comprising:

identifying an application to be utilized by the UE;

identifying network characteristics of a radio access technology (RAT) accessible by the UE;

selecting the RAT for computational offloading of the application based on the network characteristics; and

transmitting a computational offloading request via the RAT to a node associated with the RAT for execution of the application.

11. The method of claim 10 , wherein the RAT is local wireless fidelity and the node is a wireless fidelity access point.

12. The method of claim 10 , wherein the RAT is a direct wireless connection between the UE and a user computing device.

13. The method of claim 10 , further comprising:

calculating an energy consumption value for a transmission of a data volume metric via the RAT using a radio of the UE; and

selecting the RAT in part based on the energy consumption value.

14. The method of claim 10 , further comprising determining a link quality metric for a communication channel between the UE and a base station of the MEC network, wherein the RAT is selected in part based on the link quality metric.

15. The method of claim 14 , wherein the base station is an evolved NodeB (eNB) base station.

16. The method of claim 10 , wherein the MEC network includes a fifth generation (5G) new radio technology.

17. The method of claim 10 , further comprising determining a battery state of the UE, wherein the RAT is selected in part based on the battery state of the UE.

18. The method of claim 10 , further comprising determining a frequency band for the RAT, wherein the RAT is selected based on the determined frequency band.

19. At least one non-transitory machine-readable medium comprising instructions for offloading computational workloads from a user equipment (UE) in multi-access edge computing (MEC) network that, when executed by at least one processor, cause the at least one processor to perform operations to:

identify an application to be utilized by the UE;

identify network characteristics of a radio access technology (RAT) accessible by the UE;

select the RAT for computational offloading of the application based on the network characteristics; and

transmit a computational offloading request via the RAT to a node associated with the RAT for execution of the application.

20. The at least one non-transitory machine-readable medium of claim 19 , wherein the RAT is local wireless fidelity and the node is a wireless fidelity access point.

21. The at least one non-transitory machine-readable medium of claim 19 , wherein the RAT is a direct wireless connection between the UE and a user computing device.

22. The at least one non-transitory machine-readable medium of claim 19 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:

calculate an energy consumption value for a transmission of a data volume metric via the RAT using a radio of the UE; and

select the RAT in part based on the energy consumption value.

23. The at least one non-transitory machine-readable medium of claim 19 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to determine a link quality metric for a communication channel between the UE and a base station of the MEC network, wherein the RAT is selected in part based on the link quality metric.

24. The at least one non-transitory machine-readable medium of claim 23 , wherein the base station is an evolved NodeB (eNB) base station.

25. The at least one non-transitory machine-readable medium of claim 19 , wherein the MEC network includes a fifth generation (5G) new radio technology.

26. The at least one non-transitory machine-readable medium of claim 19 , further comprising instructions that cause the at least one processor to perform operations to determine a battery state of the UE, wherein the RAT is selected in part based on the battery state of the UE.

27. The at least one non-transitory machine-readable medium of claim 19 , further comprising that cause the at least one processor to perform operations to determine a frequency band for the RAT, wherein the RAT is selected based on the determined frequency band.

Continuity (6)
Continuation 17347961 · Jun 15, 2021
Continuation 16554824 · Aug 29, 2019
Continuation 15855652 · Dec 27, 2017
Provisional Application 62593169 · Nov 30, 2017
Provisional Application 62439759 · Dec 28, 2016
Related Publication 20240015203A1 · Jan 11, 2024
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