Resource optimal allocation method suitable for edge computing environment

Abstract

The invention discloses a resource optimal allocation method suitable for an edge computing environment. The method comprises the steps of obtaining a current resource utilization rate of each processing node; obtaining the current residual resource quantity of each processing node in the processing node set; selecting a processing node with the maximum sum of the current residual resources from the processing node set, and setting the processing node as a processing node o; and selecting a task k from the remaining task set, adding the task k into the processing node o, recalculating the resource balance degree Pok of the processing node o after the task k is added, and performing resource optimization allocation based on the resource balance degree. On the premise of fully considering the resource load constraint condition of each edge computing device or processing node, the appropriate task is selected from the request tasks to be allocated to the appropriate edge computing deviceor node, so that the system resource utilization rate is highest, and the overall performance of the system is optimal.

Description

Technical field [0001] The invention relates to the technical field of edge computing, in particular to a resource optimization allocation method suitable for an edge computing environment. Background technique [0002] With the introduction and deepening of the concept of ubiquitous power Internet of Things, the existing cloud computing-related technologies have been difficult to efficiently process the massive data generated by network edge devices. The development of ubiquitous power Internet of Things application requirements objectively promotes the rapid development of edge computing models, enabling them to increase task execution and data analysis capabilities on network edge devices, and migrate some or all of the computing tasks of the original cloud computing model to At the edge of the network, it reduces the computing load of the cloud computing center, reduces the pressure on the network bandwidth, and improves the efficiency of data processing. In the edge computi...

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Problems solved by technology

However, this is obviously an NP problem. In practical applications, it is impossible to find a reasonable method for polynomial time optimization. Therefore, we can only retreat to find an effective suboptimal algorithm

However, many existing heuristic algorithms, such as LPT algorithm, MULTIFIT algorithm, LPT algorithm combined with MULTIFIT algorithm and BoundFit algorithm, mostly simplify the system, only focus on a certain type of resources, and the optimal allocation method does not make the system as a whole To achieve the optimum, it is difficult to adapt and meet the requirements of reasonable scheduling and optimal allocation of resources in the edge computing environment

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