Edge cloud cooperative multi-task scheduling method and system for guaranteeing edge cloud load ratio

Abstract

The invention provides a side-cloud collaborative multi-task scheduling method and system for guaranteeing a side-cloud load ratio, and the method comprises the steps: calculating the score of each task scheduled to any node based on the remaining available amount of CPU resources and the remaining available amount of memory resources of each node, and the CPU resource request amount and the memory resource request amount of each task, establishing a score matrix; initializing a pheromone matrix; obtaining all element values based on each task, and calculating a random factor of each task; establishing a scheduling strategy boundary, and enabling ants to adopt a pheromone concentration scheduling strategy or a random scheduling strategy; obtaining a scheduling strategy of each ant, and obtaining a scheduling scheme of each ant; calculating an edge cloud load ratio, and calculating a target score of the scheduling scheme; obtaining the size of the random factor of each task, and if the size of the random factor is greater than a preset first threshold value, taking the scheduling scheme of the ant with the maximum target score as an optimal strategy; and if the information is smaller than or equal to the preset first threshold value, updating the pheromone matrix.

Description

technical field [0001] The present invention relates to the technical field of edge computing, in particular to an edge-cloud collaborative multi-task scheduling method and system that guarantees an edge-cloud load ratio. Background technique [0002] In the face of the connection of massive user equipment, the explosive growth of data traffic, and the increasing demand of users for service quality, more and more enterprises have begun to pay attention to edge computing. The rise of the edge computing model does not mean the decline of the cloud computing model. The two are not an alternative. Edge computing can actually be seen as a supplement to centralized cloud computing, providing users with better service quality. Compared with the edge cloud, the central cloud can store more data, which is convenient for global query comparison and analysis. Edge computing can better realize real-time data analysis and intelligent processing. The two have their own advantages, and t...

AI Technical Summary

Problems solved by technology

The scheduling object of the built-in scheduling strategy in kubernetes is a single task. Even if a series of tasks come at the same time, they are screened, scored and scheduled one by one. In this way, what can be achieved is a local optimum. From a global perspective The effect is not ideal, and there are relatively few existing scheduling strategies for global optimization when a large number of tasks arrive.

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