The rapid development of big data technology and artificial intelligence technology has promoted the transformation of operation and maintenance management from traditional manual operation and maintenance to intelligent operation and maintenance. AIOps is the abbreviation of Artificial Intelligence for IT OperaTIons, which is the application of artificial intelligence algorithms such as machine learning and deep learning to large data sets collected by IT operation and maintenance tools and business systems, and attempts to simulate human behavior (such as discovery, judgment, and response) Intelligent operation and maintenance management platform. Intelligent operation and maintenance AIOps enables operation and maintenance management to have algorithms and machine learning capabilities, and through continuous learning, it frees operation and maintenance personnel from complicated alarms and makes operation and maintenance intelligent. According to Gartner's forecast, the adoption rate of AIOps in 2020 will reach 40% of the entire operation and maintenance industry.
Specific applications of artificial intelligence in AIOps
Traditional operation and maintenance methods have obvious deficiencies in monitoring, problem discovery, alarms, and fault handling. They need to rely heavily on human experience, and work efficiency is low, and they are efficient in data collection, abnormal diagnosis and analysis, alarm events, and fault handling. Other aspects need to be improved. So, can AIOps supported by AI technology solve these problems? Below we introduce the application and value of AI technology in each stage from the four stages of monitoring, problem discovery, warning, and disposal.
Intelligent monitoring
Enterprises use a large number of monitoring tools such as APM, NPM, logs, DEM, infrastructure monitoring, etc., to realize the monitoring of various technology stacks. However, a large amount of invalid/useless data will increase the pressure of back-end data processing, and the omission of data may cause problems, failures and omissions. In addition, monitoring tools require a lot of manual debugging and configuration, relying heavily on the experience of operation and maintenance personnel, and labor costs are huge . In intelligent operation and maintenance, intelligent data filtering, key data identification, collection density and frequency adjustment, and performance balance of collection servers are realized through intelligent data collectors based on machine learning algorithms, thereby improving the accuracy of data collection and minimizing human effort. Intervention degree, reduce labor cost and improve operation and maintenance management efficiency.
Intelligent problem discovery
The expansion of the scale of enterprise IT systems and the complexity of the operation and maintenance environment have made it more and more difficult for operation and maintenance personnel to discover problems from massive amounts of data. AIOps can help operation and maintenance personnel quickly locate problems, trace the root causes of failures, and realize failure prediction and early warning through intelligent abnormality detection, failure correlation analysis, failure root cause analysis, and intelligent abnormality prediction.
Taking intelligent anomaly detection as an example, through AI technologies such as anomaly detection (LOF) method based on density algorithm, fast anomaly detection method based on Ensemble, anomaly detection based on historical data model, etc., it can automatically, real-time and accurately obtain monitoring data. Anomalies are found in the process, which provides a basis for subsequent failure analysis and handling.
To analyze the root cause of the failure is to trace back to the crux of the failure among the many factors that may cause the failure, and find the fundamental solution. Using machine learning or deep learning methods can find out the strong correlation between different factors, and use these relationships to infer which factors are the fundamental factors, helping users quickly diagnose problems, improve fault location speed and repair efficiency .
In addition, failures often do not exist independently. The Hain law tells us that any unsafe accident can be prevented. Intelligent anomaly prediction realizes the early diagnosis of faults by learning the prediction algorithm of important characteristic data, thereby avoiding losses. Failure prediction scenarios include: disk failure prediction, network failure prediction, memory leak prediction, etc., which can greatly reduce the risk of operation and maintenance.
Intelligent alarm
Traditional alarm management generally uses fixed thresholds and requires operation and maintenance personnel to manually set them. This method is not only a huge workload but also relies heavily on the experience of operation and maintenance personnel. Improper threshold settings may result in alarm storms or alarm failures. When the monitoring environment changes, the original fixed threshold cannot meet the requirements of alarm management. Intelligent operation and maintenance adopts a dynamic baseline alarm method to intelligently analyze the dynamic limit of the data (that is, the data range of the current state relative to the historical moment), which makes up for the defect of artificially setting a fixed threshold in the past, intelligently analyzes the development trend of the data and analyzes the data dynamic Limit, so as to make intelligent judgments on the alarm.
Various monitoring tools will generate a large amount of alarm information. There may be a large number of redundant alarms or even an alarm storm in these alarm information, which greatly interferes with operation and maintenance personnel and reduces the efficiency of operation and maintenance. Intelligent operation and maintenance is for short-term, large number, or even continuous redundant alarms. These redundant alarms can be combined through similarity and correlation judgments, so as to provide effective alarm information for operation and maintenance personnel, which can greatly reduce operation and maintenance. The difficulty of the job.
In operation and maintenance management, if an alarm cannot be resolved for a long time, the system sends the alarm to the higher level for processing. This alarm strategy is called alarm escalation. In traditional operation and maintenance, the "fixed time interval" method is generally used to set the alarm escalation strategy, and its potential delay may cause certain losses to the business. The cloud smart intelligent operation and maintenance solution establishes a model by combing the relationship between performance and business, and analyzes the impact on the business when the performance index is abnormal. If the impact exceeds the condition, the alarm event is automatically upgraded, and the system sends a notification of the upgrade event. Deal with the corresponding alarm group to avoid business losses caused by untimely alarm handling.
Intelligent fault automatic processing
The handling of faults in traditional operation and maintenance management relies heavily on the experience of operation and maintenance personnel, but human experience cannot cover all the scope of failures. Insufficient experience of operation and maintenance personnel may result in low operation and maintenance efficiency or make wrong decisions. Intelligent operation and maintenance introduces the real-time monitoring results or predicted results of API access into the decision-making knowledge base (smart brain) to intelligently generate decision-making suggestions, and judges the processing strategies adopted according to actual results and trends, which can be manual processing or automatic processing, effectively reducing Time for troubleshooting, greatly improve the efficiency of problem solving, and improve the standardization of enterprise operation and maintenance.
The value of intelligent operation and maintenance AIOps
Thanks to the development of big data, cloud computing, and artificial intelligence technology, the traditional IT operation and maintenance model that relies heavily on human brain decision-making and manual operations has rapidly changed to today's AIOps. Especially the rapid development of artificial intelligence technology based on machine learning has helped solve a large number of pain points in traditional operation and maintenance, especially in abnormal detection, abnormal prediction, correlation analysis, root cause analysis, alarm suppression, automatic fault handling, etc. All aspects and links play a role.
Take a large financial customer of Cloud Wisdom as an example. By using the cloud Wisdom smart business operation and maintenance platform, both the overall operation and maintenance efficiency and core KPIs have been greatly improved, and IT operations have also initially achieved digitalization and intelligence. . Under the wave of artificial intelligence, intelligent business operation and maintenance supported by AI can provide enterprises with a closed loop of operation and maintenance capabilities ranging from intelligent alarms, fault prediction, fault detection and analysis, fault location to fault handling, and help enterprises to digitally transform and achieve business improvement. Health continues to grow.
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