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Abnormity detection system based on time sequence data in cloud environment

A time series data and anomaly detection technology, applied in digital transmission systems, transmission systems, data exchange networks, etc., can solve problems such as missing alarm information and receiving alarm information frequently, so as to improve operation and maintenance efficiency and reduce configuration workload Effect

Inactive Publication Date: 2019-09-06
南京维拓科技股份有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this lack of objective basis can easily lead to frequent acceptance of alarm information or omission of important alarm information.

Method used

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  • Abnormity detection system based on time sequence data in cloud environment
  • Abnormity detection system based on time sequence data in cloud environment
  • Abnormity detection system based on time sequence data in cloud environment

Examples

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Embodiment 1

[0065] Example 1: see Figure 1-Figure 3 In an example of a manufacturing industry implementation, the environment is a virtualized infrastructure, including 4 physical hosts, carrying 120 virtual hosts. It is required to monitor the CPU, memory, and disk IO indicators of this part of the device. Therefore, an anomaly detection system based on time series data is implemented. The system logically includes the following modules: Collector data collection module, data storage module, Analyst analysis module, and alarm notification module. The Collector data collection module regularly collects the business index data of the device. It is transmitted to the data storage module through the aggregation module. The storage module is used to store time series data and transmit the data to the analysis module. The Analyst analyzer module is used to analyze the time series data and push notification messages to the alarm notification module. user. From the physical deployment dimensio...

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Abstract

The invention relates to an abnormity detection system based on time sequence data in a cloud environment. The system comprises the following modules: a Collector data acquisition module, a data storage module, an Analyst analysis module and an alarm notification module. The Collector data collection module collects service index data of a device where the Collector data collection module is located at regular time and transmits the service index data to the data storage module through the convergence module. The storage module is used for storing time sequence data and transmitting the data to the analysis module, and the Analyst analyzer module is used for analyzing the time sequence data and pushing a notification message to a user rate through the alarm notification module. According to the scheme, the dynamic model is established through an algorithm to carry out abnormal detection, the operation and maintenance efficiency of the data center is improved, and the system is suitablefor centralized operation environments such as enterprise private clouds and data centers and does not depend on fixed empirical threshold setting. The monitoring system and the dynamic algorithm provided by the invention are used for detecting abnormities, so that the stiff setting of regularized alarms can be avoided.

Description

Technical field [0001] The invention relates to a detection system, in particular to an abnormality detection system based on time series data in a cloud environment, and belongs to the technical field of time series data abnormality detection. Background technique [0002] With the development of cloud technology, the scale of machines managed by data centers has grown exponentially. At the same time, the huge workload of operation and maintenance has also increased the cost of operation and maintenance. How to effectively improve the efficiency of automated operation and maintenance has also become an urgent problem to be solved. The purpose of operation and maintenance is to ensure the smooth operation of production, and system monitoring is the most basic link. In the data center, there are a large number of time-series-based monitoring indicators, all of which are time-stamped. Therefore, various trends, proportions, and abnormalities can be analyzed. In the operation and...

Claims

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Application Information

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IPC IPC(8): H04L12/26H04L29/08
CPCH04L43/08H04L43/16H04L67/10
Inventor 杨松贵谌瑞敏
Owner 南京维拓科技股份有限公司
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