Method for task offloading based on power control and resource allocation in industrial internet of things

Patent metadata

Field	Value
Publication number	US-11778018-B2
Application number	US-202217861123-A
Country	US
Kind code	B2
Filing date	Jul 8, 2022
Priority date	Jan 8, 2020
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

A method for task offloading based on power control and resource allocation in the Industrial Internet of Things includes establishing a computing model for computation tasks at different offloading locations, constructing communication power control, resource allocation and computation offloading problems as a mixed integer non-linear programming model, solving them using a deep reinforcement learning algorithm to obtain an optimal strategy for offloading of the computation tasks, thus achieving communication power optimization and cross-domain resource allocation.

First claim

The invention claimed is:

1. A method for task offloading based on power control and resource allocation in the Industrial Internet of Things, comprising steps of:
step 1: configuring an Industrial Internet of Things network,
wherein the Industrial Internet of Things network comprises a plurality of switches and a plurality of devices, the plurality of switches communicating with one another in a wired fashion,
partitioning the Industrial Internet of Things network into a plurality of cluster domains according to communication coverage ranges of the plurality of switches,
wherein each of the plurality of cluster domains comprises one edge server and at least one device of the plurality of devices, the at least one device wirelessly communicating with a switch in the cluster domain where it is in, computing capacity of the edge server being f j S , computing capacity of each of the at least one device being f i L , each of the at least one device configured to generate one computation task Q i , the computation task Q. configured to contain a task data volume indicator d i and a task computational load indicator c i ,
executing the computation task on a device of the plurality of devices, and establishing a first computing model; wherein the first computing model comprises first time consumption t i L , first energy consumption e i L and first overhead u i L ;
offloading the computation task to a first edge server for computation thereon, and establishing a second computing model; wherein the first edge server comprising an edge server in a cluster domain where the device is in, the device offloading, via a first switch, the computation task to the first edge server for computation, the first switch comprising a switch in the cluster domain where the device is in, the first edge server configured to allocate, to the computation task offloaded to the first edge server, computing resources; wherein the second computing model comprises second time consumption t i LS , second energy consumption e i LS and second overhead u i LS ;
offloading the computation task to a second edge server for computation thereon, and establishing a third computing model; wherein the second edge server comprising an edge server in another cluster domain where the device is not in, the device offloading, via the first switch and a second switch, the computation task to the second edge server for computation, the second switch comprising a switch in a cluster domain where the second edge server is in, the second edge server configured to allocate, to the computation task offloaded to the second edge server, computing resources; wherein the third computing model comprises third time consumption t i OS , third energy consumption e i OS and third overhead u i OS ;
step 2: based on the first computing model, the second computing model and the third computing model, establishing a total overhead model for all computation tasks in the Industrial Internet of Things network, constructing an objective function and constructing a mixed integer non-linear programming problem; wherein the step 2 comprises:
defining a first decision variable x i ={0,1}, wherein x i =0 means the computation task is executed at the device, while x i =1 means the computation task is offloaded to an edge server for computation;
defining a second decision variable β i ={0,1}, wherein β i =0 means the computation task is executred at the first edge server, while β i =1 means the computation task is executed at the second edge server;
defining a third decision variable γ i , wherein γ i represents the edge server that executes the computation task and γ i ∈{1,2. . . ,N},
overhead for the computation task Q i is
u i =(1−x i )u i L +x i (u i LS +β i (u i OS −u i LS ))

total overhead U for all the computation tasks in the Industrial Internet of Things network is

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constructing the objective function as follows:

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Assignees

• Univ Shanghai Jiaotong

Inventors

• Xu Jincheng
• Zhou Peng
• Yang Bo
• Chen Cailian
• Guan Xinping

Classifications

• H04L67/1008 Primary

  based on parameters of servers, e.g. available memory or workload (monitoring of computer activity G06F11/30) · CPC title

• H04L41/142 Primary

  using statistical or mathematical methods · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• H04L41/145 Primary

  involving simulating, designing, planning or modelling of a network · CPC title

• H04L43/0852

  Delays · CPC title