Distributed data multi-stage aggregation method and device, server and storage medium

Abstract

The invention relates to the technical field of the Internet, in particular to a distributed data multi-stage aggregation method and a device, a server and a storage medium, and aims to solve the technical problem that task response efficiency needs to be improved. The method is based on a plurality of edge computing centers of a current optical network, and aggregation of a plurality of stages isexecuted; wherein the aggregation of each stage comprises the following steps: determining a plurality of initial clustering center nodes corresponding to the current stage from a plurality of edge computing centers according to the current resource situation of each edge computing center in the current optical network; adjusting a plurality of initial clustering center nodes corresponding to thecurrent stage to obtain a plurality of clustering center nodes; determining an optimal grouping number corresponding to the current stage based on the plurality of clustering center nodes; accordingto an aggregation scheme corresponding to the optimal grouping number, judging whether data required by the to-be-processed task is aggregated to an edge computing center, and if yes, ending aggregation; and otherwise, carrying out polymerization of the next stage. The method can effectively reduce the task response time delay.

Description

technical field [0001] The present application relates to the technical field of the Internet, and in particular to a distributed data multi-stage aggregation method, device, server and storage medium. Background technique [0002] With the development of the Internet of Things, a large number of Internet of Things users, Internet of Things devices and sensors distributed across different places generate a large amount of data. With the rapid growth of data in the Internet of Things and the latency and bandwidth requirements of emerging services, cloud computing is facing severe challenges. On the one hand, mobile terminals need to exchange data with the cloud data center located in the core network, and the data transmission distance is relatively long, which makes it difficult to meet the business needs of ultra-low latency; on the other hand, due to the rapid increase in the number of smart terminals and IoT terminals, During the peak period of the network, the core netw...

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Problems solved by technology

[0006] This single-stage cross-regional data aggregation scheme does not consider the number and location of edge computing centers that store the required data, nor does it take into account the processing power of a single edge computing center and the limitations of network resources near the aggregation computing center. It has the following Disadvantages:

[0007] First, because the data is stored in multiple different edge computing centers, when some edge computing nodes that store the required data are far away from the aggregation center, it will cause a long transmission delay in the network and occupy too much network link. Second, when there are many edge computing centers that store the data required by the business, it is necessary to transmit the data of multiple edge computing centers to an aggregation computing center, resulting in competition for network resources near the aggregation computing center Third, the processing and storage capacity of the edge computing center is limited, and a large amount of data is transmitted to a single edge computing center for processing at the same time, resulting in an excessively long processing delay

[0008] It can be seen that the cross-distance data aggregation scheme in the prior art fails to fully consider the processing capabilities of each edge computing center in the network resources and the resource competition of the aggregation computing center, which affects the response delay of the task and slows down the response time of the task. Efficiency, there are certain limitations, it is difficult to effectively cope with the exponential growth of cross-regional data volume

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