Method for realizing service data restoration based on cross-station switching collection

Abstract

The invention discloses a method for realizing service data restoration based on cross-station switching collection. The method comprises the following steps: generating performance data collection tasks through a data collection system, releasing the collection tasks to collectors, meanwhile deploying a monitoring service for monitoring a collection task release catalogue, periodically monitoring the state of the collection task release catalogue, judging whether the collectors run abnormally, and switching the collection task of the abnormal collector to the standby normal collector. By adoption of the method disclosed by the invention, the collection continuity of performance data is realized, thereby ensuring the continuity of the performance data on which the index data analysis, problem locating and trouble removal depend, and providing reliable and stable data guarantee for the network operation personnel to analyze the performance data and locate network problems.

Description

technical field [0001] The invention relates to the technical field of network performance management systems of telecommunication operators, in particular to a method for realizing service data recovery based on cross-site switching collection. Background technique [0002] With the evolution of telecom operators' networks and the increasingly complex network environment, in order to more real-time and effectively monitor network performance and change trends, and support network operations, the data collection of network performance management systems is crucial. [0003] The actual network environment has the characteristics of multiple vendors, multiple network domains, and multiple technical fields. It is necessary to collect raw data such as performance data, configuration data, parameter data, and stock data, and provide them to upper-level applications such as Report, Dashboard, Instance Query, and TroubleShooting for network analysis. Root cause analysis and trouble...

AI Technical Summary

Problems solved by technology

[0006] 2) There are a large number of data sources, and the amount of raw data output by each data source per unit time is large, which directly leads to a huge amount of data collected by the collection node, and the raw data that is finally aggregated into the data collection system is massive data;

[0007] 3) The data source division has a vast area, and each data source and data acquisition system are in different local area networks. The Internet network connection between them has unstable network and limited bandwidth, which will have a great impact on the transmission of original data;

[0008] 4) The remote acquisition machine and the data acquisition system are in different local area networks. If the remote acquisition machine is abnormal, it will not be able to collect data normally, which will directly lead to the interruption of business data

[0009] Since different collection machines are distributed and deployed at sites in different regions, when the remote collection machine is down or abnormal, it cannot be recovered through VCS switching, which will lead to failure of normal collection and interruption of business data

[0010] Therefore, a data collection mechanism is needed to solve the problem of remote cross-network collection business data recovery

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