A low-latency task scheduling method for dynamic fog computing network

Abstract

The present invention provides a low-latency task scheduling method for a dynamic fog computing network. The helper nodes in the network will not broadcast their own node status information in real time, such as task queue information and shareable computing resource information, or the helper nodes will not broadcast their own node status information in real time. Requests for this information will not be responded to in real time. Every time there is a task unloading requirement, the task node needs to make an unloading decision in real time, and select an unloading task from the current candidate helper nodes. Since the task node is unknown to the state of the helper node, and the task itself also has a delay requirement, the task node needs to learn from its past task offloading experience to provide judgment for the current decision. The present invention proposes a one-to-multiple task offloading algorithm based on an online learning method for a non-dynamic and dynamically changing fog computing or edge computing network, which can greatly reduce the energy overhead caused by information dissemination in the network and prolong the task nodes and the duration of use of the helper node.

Description

technical field [0001] The invention belongs to the field of computing and communication networks, and in particular relates to a task unloading algorithm aiming at reducing the average unloading time delay of tasks. Background technique [0002] With the rise and development of technologies such as the intelligent Internet of Things, 5G, and artificial intelligence, processing massive and diverse data and meeting ultra-low service latency requirements have become increasingly urgent problems to be solved. The traditional centralized cloud computing architecture has a large delay due to the long distance between the terminal device and the cloud server. access. In this context, fog computing came into being. With its distributed framework and low-latency services, fog computing is expected to become a key technology supporting future intelligent IoT, 5G and artificial intelligence applications, and has received extensive attention and research in recent years. Fog computi...

AI Technical Summary

Problems solved by technology

Continuously broadcasting and monitoring node status information in real time will generate a large amount of energy consumption and affect the service life of the equipment. In the future ultra-large-scale system, this problem will become more prominent

[0006] In the actual situation, nodes will dynamically enter and exit the network, and the shareable computing resources will also change with the situation. At the same time, it is hoped to minimize the energy consumption in the network and prolong the service life of the equipment. Such a problem that is most close to the actual demand and needs to be solved , but few scholars have studied

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