Resource monitoring data preprocessing system in edge computing scene

Abstract

The invention relates to a resource monitoring data preprocessing system in an edge computing scene, and the system comprises: an end equipment collection unit which collects monitoring flow data generated by specified end equipment in a cloud edge cluster, and transmits the monitoring flow data to a flow processing server corresponding to an edge node; an edge host collection unit which is used for collecting the hardware resource use condition of the edge host through a container deployed in the edge host; a storage unit which receives the monitoring stream data returned by the stream processing server from the preprocessing unit, compresses the monitoring stream data and stores the compressed monitoring stream data; a preprocessing unit which is a local server, captures the data collected by the collection unit according to a configuration file provided by a user, processes the data and sends the data to the storage unit for storage or transmits the data to the cloud side through the cloud side data transmission unit; and a cloud side data transmission unit which is used for sending the processed data to the cloud side, wherein each unit is uniformly issued and deployed to the edge end by the cloud in a yaml form. The system can discover the abnormality of the end equipment and the edge host in time, and greatly improves the efficiency of monitoring automation.

Description

technical field [0001] The invention relates to the field of edge computing, in particular to a resource monitoring data preprocessing system in an edge computing scenario. Background technique [0002] Originating from the media field, edge computing refers to an open platform that integrates network, computing, storage, and application core capabilities on the side close to the source of objects or data, and provides the nearest end services. Its applications are launched on the edge side to generate faster network service responses and meet the basic needs of the industry in terms of real-time business, application intelligence, security and privacy protection. Edge computing sits between the physical entity and the industrial connection, or on top of the physical entity. With cloud computing, historical data from edge computing can still be accessed. [0003] Kubernetes is a popular container orchestration system today. It is an open source container cluster management...

AI Technical Summary

Problems solved by technology

[0006] With the advent of the era of 5G and the Internet of Things, the amount of resource monitoring data on the edge of the network will be very large. If the data is handed over to the cloud management platform for processing, it will result in huge real network traffic and low latency of some devices. As a result, it is difficult to guarantee real-time monitoring work, the data security risk of special monitoring information is greatly increased, and there are many end-device data transmission protocols that cannot be unified. Using edge computing, massive monitoring data can be processed nearby, and a large number of device status information can be real-time. With monitoring, the above problems can be easily solved

Therefore, the traditional cloud center-based Kubernetes monitoring solution can no longer adapt to the current edge computing scenario, and a resource monitoring data preprocessing method on edge nodes is needed.

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