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Task analysis and resource allocation method and system based on formalized 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, unbalanced load, increased leakage of user privacy data, etc., and achieves the effect of good scalability

Active Publication Date: 2021-06-22
SHANGHAI JIAO TONG 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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  • Task analysis and resource allocation method and system based on formalized model
  • Task analysis and resource allocation method and system based on formalized model
  • Task analysis and resource allocation method and system based on formalized model

Examples

Experimental program
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Embodiment 1

[0051] According to a formal model-based task analysis and resource allocation method provided by the present invention, it includes:

[0052] Step S1: The task analysis module analyzes the task requirements, and formally expresses the task requirements in the form of task workflow;

[0053] Step S2: The resource allocation module merges the formalized task workflow based on reducing the total amount of resources consumed by the workflow to obtain the combined task workflow;

[0054] Step S3: abstract the resource capability of the mobile terminal device into a meta-model, and perform service registration on the cloud;

[0055] Step S4: Based on the merged task workflow and services registered in the cloud, use a heuristic algorithm to schedule and match tasks;

[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: Use the method of combining pattern matchin...

Embodiment 2

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

[0083] In order to solve the task and resource management problems existing in mobile edge computing, the present invention proposes a formal model-based task analysis and resource allocation method for cross-domain resource collaboration. The method is mainly divided into three modules, task parsing module, resource allocation module and 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 Internet of Things 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 these Two models implement heuristic algorithms to generate task allocation plans for the final execution of tasks. This method can reduce the overhead of repeated task parsing and find a relatively op...

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 this invention mainly protects the task matching method based on mobile edge computing, and each module in the system has various implementation methods, so only one feasible strategy is given.

[0098] Such as 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 formalize task workflow;

[0100]Resource allocation module: used to combine formalized workflows and 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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Abstract

The invention provides a task analysis and resource allocation method and system based on a formalized model. The method comprises the steps: S1, analyzing a task demand through a task analysis module, and carrying out the formalized representation of the task demand through a task workflow; s2, combining the formalized task workflows by a resource allocation module to obtain combined task workflows; s3, abstracting the resource capability of the mobile terminal device into a meta-model, and carrying out service registration on the cloud; s4, based on the merged task workflow and the service registered in the cloud, scheduling matching of the tasks is carried out by adopting a heuristic algorithm; and S5, the mobile terminal equipment 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 specifically, to a method and system for task analysis and resource allocation based on a formal model for mobile edge computing scenarios. system. Background technique [0002] As edge computing continues to evolve, it can circumvent problems such as long distances, capacity constraints, multiple network hops, and centralized processing of loads in traditional Internet architectures. 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 deficien...

Claims

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Application Information

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Patent Type & Authority Applications(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 JIAO TONG UNIV
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