What is AIOps?

The intelligent O&M AIOPS platform often uses big data, machine learning, and visualization to make IT O&M more efficient. Enterprise infrastructure and operations leaders should start deploying an AIOPS platform as early as possible, optimize current performance analytics, and expand into IT service management and automation over the next two to five years.

The AIOps platform is a software system that combines big data and machine learning functions, and mainly collects and analyzes the volume, type, and speed of data continuously generated by the IT system to support the main functions of IT operation and maintenance. The platform enables the simultaneous use of multiple data sources, data collection methods, data analysis and presentation techniques.

AIOps can be applied to a wide range of IT O&M processes and scenarios, including performance analysis, anomaly detection, event correlation analysis, IT service management, and automation.

Core features include:

Extract data from a variety of data sources.

Real-time analysis of the extracted data.

Historical analysis of stored data.

Provides a data access interface.

Store the acquired data.

Use machine learning techniques.

Initiates actions based on the results of the analysis.

AIOps is becoming increasingly dominant in the enterprise, and some mature organizations are already using the technology to support business leaders' decision-making.

According to Gartner's latest explanation, AIOPS intelligent O&M refers to the integration of big data and machine learning capabilities to extract and analyze growing IT data in three dimensions: volume, variety, and velocity through loose coupling and scalability, thereby providing support for IT O&M management products.

Since the development of O&M, OPS has moved from manual O&M, process-based standardized O&M, and platform-based automated O&M to DevOps. In recent years, OPS has been integrated with big data and AI, extending to DataOPS and AIOPS. This is a historical necessity, and it will also bring extremely high efficiency to the IT operation and maintenance of enterprises, for enterprises.

AIOps means higher efficiency, lower costs, and shorter time to resolution.

Compared with traditional O&M tools, AIOPS has obvious advantages: the metric collection dimension of traditional O&M tools is too simple, and when judging faults, they will use a lot of O&M indicators to troubleshoot, which will cause a waste of time, and traditional O&M data is more dependent on expert experience.

AIOPS can be analyzed through the underlying big data platform, and through the full learning and judgment of AI technology, the alarm can be directly traced and reduced, and the root cause and location of the fault can be displayed to the O&M personnel in the first time, which greatly improves the work efficiency and the time to deal with the fault.

As early as 2016, Gartner, a well-known IT research institution, added the term AIOPS to its thesaurus, AIOPS is the abbreviation of Algorithmic IT Operations, which is literally understood as an algorithm-based operation and maintenance method. For example, the best domestic AIOPS vendors have Tingyun, which has the leading data acquisition capabilities in China, and has been continuously recommended by Gartner as a key service provider in the field of AIOPS.

False recruitment of Bishi Technology is regarded as an outsourcing industry, and you can do everything.

Intelligent O&M is a new digital O&M capability and a must-have capability for digital transformation. Compared with the traditional O&M model, intelligent O&M can improve performance in four aspects:

O&M data governance. The high-performance real-time processing data platform widely collects, processes, and analyzes diversified O&M data in the process of digital business operation, including alarms, metrics, logs, configurations, and O&M tickets, which not only improves the governance capability of O&M big data, optimizes data quality, but also lays a good foundation for further activating the value of O&M data.

Business digitalization risks. This enables O&M personnel to not only improve the analysis capabilities of historical O&M data, but also perform anomaly detection and problem prediction on real-time data, effectively reducing the operational risks of digital services and improving availability and stability.

O&M labor costs. It makes it possible to locate the root cause in the true sense, reduces the dependence on the experience and skills of professional operation and maintenance personnel, quickly shortens the troubleshooting time and effectively reduces labor costs;

Business-side influence. From a business perspective, we can use diversified data to improve operational analysis and decision-making capabilities, such as end-to-end analysis of business transaction status, provide timely feedback and decision-making support basis to business and customer service departments, and fully enhance business influence.

It can be seen that the AIOPS-based management method has subverted the underlying technology of monitored O&M. Traditional IT operations management tools are more focused on incidents (i.e., alerts), configuration, and performance, while AIOPS is more focused on issues, analytics, and performance.

With the advent of the era of artificial intelligence, the importance of IT technology has become more and more prominent, and O&M has also entered a new era of O&M, that is, AIOPS. Through technology investment and practice in recent years, AIOps has achieved remarkable results in O&M scenarios such as efficiency improvement, availability assurance, and cost optimization. Compared with traditional O&M tools, AIOps has obvious advantages:

The metric collection dimension of traditional O&M tools is too simple, and when judging faults, they will use a lot of O&M indicators to troubleshoot, which will cause a waste of time, and traditional O&M data is more dependent on expert experienceAIOPS can be analyzed through the underlying big data platform, and through the full learning and judgment of AI technology, the alarm can be directly traced and reduced, and the root cause and location of the fault can be displayed to the O&M personnel in the first time, which greatly improves the work efficiency and the time to deal with the fault. There are many advantages of AIOPS, and you can consult professional companies for specific advantages and functions, such as Tingyun, a third-party testing service provider currently engaged in application performance management (APM) and user experience optimization in China, and its business has now covered major industries such as **, finance, operators, Internet, aviation, energy and power, industrial manufacturing, education, etc., providing services for thousands of well-known enterprises and winning wide trust and recognition.

Indeed, I have heard that some people are worried and misunderstood.

In fact, the main driving force for the emergence and vigorous development of the AIops platform is that the amount of data in the IT system of enterprises is getting larger and larger, and the structure of the enterprise is becoming more and more complex, and it is difficult to deal with the endless operation and maintenance problems by relying on the experience and manual judgment of simple operation and maintenance engineers. Traditional O&M monitoring software can display O&M data, but it cannot provide analysis and disposal suggestions, and cannot keep up with the requirements of troubleshooting.

With the application of AI, the operation and maintenance of the megasensitive data can be better analyzed, or provide the location of suspected root causes, or provide early warning of abnormalities, using the Rotchaibi AIOPS system, MTTR (average troubleshooting time) can be shortened from hours to minutes, and the experience of troubleshooting can also be stored as existing knowledge for others to refer to.

Therefore, after AIOPS, one operation and maintenance engineer managed N applications and devicesEven if the device is increased to 100N, it may be managed by one engineer, instead of increasing to 100 operation and maintenance engineers in equal proportions. That's what technological progress is all about.

AIOps is the most direct impact of artificial intelligence and machine learning on IT operations in the past two years, that is, the artificial intelligence IT operation platform. It includes a series of technical capabilities, which is very wide, and it can be considered that all big data and artificial intelligence and machine learning technologies under IT operations management are AIOPS technologies. These technologies can greatly enhance traditional IT operations capabilities, including enhancements in service desk, automation, and monitoring.

From the perspective of current practice, AIOPS is in the early stage, and some applications have not yet been fully rolled out, but there are also active attempts, such as dynamic baseline intelligent alarms in monitoring performance management. The application needs to be based on strong big data capabilities. I saw that Borui data mentioned their dynamic baseline, which can intelligently alarm and reduce false positives.

The AIOPS technology platform is a software system that combines big data and machine learning or deep learning functions to enhance and partially replace a wide range of IT operation processes and tasks.

This question is from 2018. After two years of development, intelligent O&M AIops has become more mature than before, and there are many more mature cases.

For example, IT O&M engineers encounter alarm storms on a daily basis.

It's annoying to be interrupted by countless alarm notifications during breaks. In AIOPS, the alarm convergence function allows you to aggregate and converge the alarm SMS messages that were originally set based on the metric threshold, and only inform you of the most important problems. This greatly reduces the frequency of engineers being harassed by "Dididi" text messages or WeChat notifications, and can also help engineers determine the problem more quickly without being drowned in a large number of invalid messages.