Method and system for task analysis and resource allocation based on formal model

A technology for resource allocation and tasks, applied in the field of computer science, can solve the problems of low task completion rate of edge nodes, real-time requirements of the Internet of Everything, task failure, etc., and achieve the effect of good scalability

Active Publication Date: 2022-07-26
SHANGHAI JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the number of devices connected to the Internet is already very large, and the amount of daily data is also increasing rapidly. The traditional cloud computing model is to upload all data to the cloud computing center through the network, and use the super computing power of cloud computing to solve the calculation of centralized applications. demand problem, but it has three shortcomings: 1. The real-time requirements of the Internet of Everything
With the popularity of smart homes, many families install network cameras in the house, and directly uploading the video data collected by the camera to the cloud computing center will increase the risk of leaking user privacy data
3. High energy consumption
The performance of edge nodes in the edge computing model is far inferior to that of cloud service clusters. Therefore, when different tasks arrive, if the task is simply randomly assigned to an edge node, or the effect of the algorithm is not good enough, then there will be a low task completion rate or edge The problem of node load imbalance or even task failure

Method used

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  • Method and system for task analysis and resource allocation based on formal model
  • Method and system for task analysis and resource allocation based on formal model
  • Method and system for task analysis and resource allocation based on formal model

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Embodiment 1

[0051] A method for task analysis and resource allocation based on a formal model provided according to the present invention, comprising:

[0052] Step S1: the task analysis module analyzes the task requirements, and formally expresses the task requirements as a task workflow;

[0053] Step S2: the resource allocation module merges the formalized task workflows based on the total resources consumed by the reduced workflows to obtain a combined task workflow;

[0054] Step S3: abstracting the resource capabilities of the mobile terminal device into a meta model, and performing service registration in the cloud;

[0055] Step S4: Based on the combined task workflow and the service registered in the cloud, a heuristic algorithm is used to perform task scheduling and matching;

[0056] Step S5: The mobile terminal device executes the task according to the scheduling matching result.

[0057] Specifically, the step S1 includes:

[0058] Step S1.1: using the method of combining ...

Embodiment 2

[0082] Embodiment 2 is a preferred example of Embodiment 1

[0083] In order to solve the task and resource management problems in mobile edge computing, the present invention proposes a formal model-based task analysis and resource allocation method for cross-domain resource cooperation. The method is mainly divided into three modules, a task analysis module, a resource allocation module and a mobile Internet of Things device module.

[0084] The task parsing module is used to abstract a batch of tasks into a resource requirement description meta-model, and the mobile IoT device module is used to abstract the device's ability to provide resources into a resource supply description meta-model and issue tasks. The resource allocation module is a combination of this. Two models are used to perform heuristics to generate task assignment plans for the final execution of tasks. This method can reduce the overhead of repetitive task parsing and find a relatively optimal task alloca...

Embodiment 3

[0096] Embodiment 3 is a preferred example of Embodiment 1 and / or Embodiment 2

[0097] The content of the present invention will be described in detail below in conjunction with specific implementation methods. Because the present invention mainly protects the task matching method based on mobile edge computing, and each module inside the system has various implementation methods, only one feasible strategy is given.

[0098] like Figure 1-5 As shown, the system architecture of the present invention is divided into three parts:

[0099] Task analysis module: used to analyze large-scale task requirements and formally represent task workflow;

[0100]Resource allocation module: used to merge formal workflows, and then match services with subtasks;

[0101] Mobile IoT device module: the carrier of task execution;

[0102] Specifically, the task parsing module includes the following:

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PUM

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Abstract

The present invention provides a method and system for task analysis and resource allocation based on a formal model, including: step S1: a task analysis module analyzes task requirements, and formally expresses the task requirements as a task workflow; step S2: a resource allocation module Combine the formalized task workflows to obtain a combined task workflow; step S3: abstract the resource capabilities of the mobile terminal device into a meta model, and perform service registration in the cloud; step S4: based on the combined task workflow and For the service registered in the cloud, a heuristic algorithm is used to perform task scheduling and matching; step S5 : the mobile terminal device executes the task according to the scheduling matching result.

Description

technical field [0001] The present invention relates to the field of computer science, in particular, to a method and system for task analysis and resource allocation based on a formal model, and more particularly, to a method and system for task analysis and resource allocation based on a formal model oriented to mobile edge computing scenarios. system. Background technique [0002] As edge computing continues to develop, it can circumvent the long distances, capacity constraints, multiple network hops, and centralized handling of loads in traditional Internet architectures. At present, the number of devices connected to the Internet has become very large, and the amount of data is also increasing every day. The traditional cloud computing model is to upload all data to the cloud computing center through the network, and use the super computing power of cloud computing to solve the calculation of centralized applications. However, it has three shortcomings: 1. The real-tim...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/48G06F9/50G06N3/00
CPCG06F9/4843G06F9/5027G06F9/5072G06N3/006
Inventor 张政童孙士勇陈昊鹏王见思黄子昂
Owner SHANGHAI JIAOTONG UNIV
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