US 11,956,307 B1
Distributed task offloading and computing resources management method based on energy harvesting
Yun Li, Chongqing (CN); Zhixiu Yao, Chongqing (CN); Shichao Xia, Chongqing (CN); and Guangfu Wu, Chongqing (CN)
Assigned to CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, Chongqing (CN)
Appl. No. 17/909,374
Filed by CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, Chongqing (CN)
PCT Filed Nov. 4, 2021, PCT No. PCT/CN2021/128685
§ 371(c)(1), (2) Date Sep. 3, 2022,
PCT Pub. No. WO2022/199036, PCT Pub. Date Sep. 29, 2022.
Claims priority of application No. 202110312344.6 (CN), filed on Mar. 24, 2021.
Int. Cl. H04L 67/1023 (2022.01)
CPC H04L 67/1023 (2013.01) 9 Claims
OG exemplary drawing
 
1. A distributed task offloading and computing resources management method based on energy harvesting, comprising:
establishing, based on a mobile edge computing environment, a task local computing model and an edge cloud computing model;
obtaining a benefit obtained by a device purchasing resources from each of mobile edge computing servers for performing task offloading, performing a perturbation Lyapunov optimization algorithm at the device to ensure an energy level of a battery and stability of a task queue at the device, and establishing a device maximum benefit objective function for the device based on the perturbation Lyapunov optimization algorithm;
for each of the mobile edge computing servers, obtaining a benefit of the mobile edge computing server providing a computing service for the device, and establishing a mobile edge computing server maximum benefit objective function;
determining, based on a task backlog of the device, the energy level of the battery of the device and a quotation of each of the mobile edge computing servers, a mobile edge computing server pre-screening criteria, and pre-selecting, by the device based on the pre-screening criteria, a mobile edge computing server for performing task offloading;
calculating, by the device based on a device maximum benefit problem based on the perturbation Lyapunov optimization algorithm, an optimal task size strategy for performing task offloading by the device to the pre-selected mobile edge computing server based on a Lagrange multiplier algorithm and a KKT condition in each of time slots;
for each of the mobile edge computing servers, obtaining, by the mobile edge computing server based on the optimal task size strategy for performing task offloading by the device to the selected mobile edge computing server and a mobile edge computing server maximum benefit problem, an optimal quotation strategy of the mobile edge computing server for the device in each of the time slots; and
in a case that the optimal task size strategy for performing task offloading by the device to the pre-selected mobile edge computing server meets a Stackelberg equilibrium and an optimal dynamic quotation strategy of a mobile edge computing server for the device meets the Stackelberg equilibrium, performing, by the device, task offloading to the mobile edge computing server based on an optimal task offloading strategy.