Edge cloud collaborative task scheduling method and system based on deadline perception

Abstract

The invention belongs to the technical field of computer application, and discloses an edge cloud collaborative task scheduling method and system based on deadline perception, and the method comprises the steps: transmitting terminal equipment data to a unified access module; pre-classifying the tasks, and sending the tasks to different destinations respectively; the task scheduling center extracting a task type and task deadline, and querying a server set Stargetl in which corresponding services are installed; assigning the task to a server in the Starget1, calculating whether the time delay satisfies the deadline, and assigning the task to the server satisfying the condition; and when the task arrives at the target server, generating a new task execution queue. According to the invention, the unified access module receives the task data generated by the terminal equipment, pre-classifies the tasks and sends the pre-classified tasks to the remote cloud center or the task scheduling center, so that the load balance of the edge cloud system is ensured, and the resource utilization rate of the whole edge cloud architecture is improved.

Description

technical field [0001] The invention belongs to the technical field of computer applications, and in particular relates to a method and system for scheduling edge-cloud collaborative tasks based on deadline perception. Background technique [0002] At present, with the rapid development of technologies such as the Internet of Things and the mobile Internet, the data volume of terminal devices is surging. The continuous and rapid growth of data volume has promoted the evolution of the entire computing model, and at the same time puts forward higher requirements for data storage and processing technologies, especially the real-time processing and intelligent analysis of network edge services. Thanks to cloud computing technology, more and more mobile applications and IoT devices can utilize the abundant computing resources of remote cloud centers to handle local computing-intensive tasks (such as image processing tasks). In theory, this offloading of tasks can significantly e...

AI Technical Summary

Problems solved by technology

[0005] (1) The load balancing of servers in the edge cloud system is neglected in the prior art, and the overall resource utilization rate is not high

[0006] (2) In the task scheduling algorithm, factors such as server network resources, computing resources, storage resources, task completion deadlines, and overall service quality are not considered comprehensively. Most technologies only consider part of the influencing factors, resulting in the inability to achieve optimal scheduling results.

[0007] The difficulty of ...

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