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A qoe-aware edge computing task scheduling method

A task scheduling and task technology, applied in the transmission system, electrical components, etc., can solve problems such as equipment load imbalance, task execution efficiency impact, and inability to process data in real time, and achieve the effect of minimizing execution time

Active Publication Date: 2020-08-21
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

First, the computing power of cloud computing cannot process massive amounts of data in real time
In the Internet of Things environment, the amount of data is growing explosively. Although cloud computing technology can improve the computing power of the system by increasing the number of devices in the cluster, it still cannot meet the requirements of real-time computing of massive data.
Second, a large amount of data is transmitted from the network device to the cloud, which increases the network load and causes excessive network delay.
The traditional task scheduling algorithm does not consider the impact of dynamic network status changes and differences in device resources on task execution time. There are certain defects that may lead to unbalanced device loads and have a certain impact on task execution efficiency.

Method used

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  • A qoe-aware edge computing task scheduling method
  • A qoe-aware edge computing task scheduling method
  • A qoe-aware edge computing task scheduling method

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Embodiment Construction

[0018] Specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0019] The present invention implements a QoE-aware edge computing task scheduling method, which specifically includes the following steps:

[0020] Step 1: Run the system data collection tool on the router to periodically collect all device resource status information and network status information in the network. Specific information includes: Maximum CPU load for each device (Generally the same as the number of CPU cores); the current CPU load of each device The network bandwidth P between each device and the router i , the round-trip delay R between each device and the router i .

[0021] Step 2: Predict the time required for the current task to run on different devices and the CPU resources consumed by the task (increased value of CPU load caused by task execution). In our scheduling model, the execution time of a task on a device is...

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Abstract

A QoE-aware edge computing task scheduling method. The steps are: the router periodically collects device resource status information and network status information. When the router receives a task scheduling request, it first predicts the execution time of the task on different devices according to the prediction model. On this basis, an edge computing task scheduling model is established based on device resource status information, network status information, and task data, and a linear programming model solver is used to solve the task scheduling model to obtain task scheduling results. Finally, based on the task scheduling result, the router is responsible for distributing the task to the corresponding edge device / cloud server for execution, and returning the result to the task requesting device.

Description

technical field [0001] The invention relates to a method for scheduling edge computing tasks based on QoE perception. Background technique [0002] The development of Internet of Things technology has brought massive heterogeneous data and a large number of Internet of Things devices. In the field of video surveillance, an 8-megapixel camera generates 3.6GB of data per hour, and the amount of data in a city reaches hundreds of petabytes a month. Currently, most IoT data processing relies on cloud computing technology. The increase in data volume has brought new challenges to cloud computing. First, the computing power of cloud computing cannot process massive amounts of data in real time. In the Internet of Things environment, the amount of data is growing explosively. Although cloud computing technology can improve the computing power of the system by increasing the number of devices in the cluster, it still cannot meet the requirements of real-time computing of massive ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08
CPCH04L67/12H04L67/60
Inventor 董玮卜佳俊高艺张甲栋管高扬
Owner ZHEJIANG UNIV
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