In this article, we will explain “AIOps”, which is attracting attention as an operation method that can automate system operations and improve operation efficiency by utilizing AI and machine learning.
With the rapid progress of DX and digitalization, various systems and services are being developed, the amount of data handled by businesses continues to increase, and enormous costs and resources are required for their operation.
In the report ” Survey on IT Human Resources Supply and Demand ” released by the Ministry of Economy, Trade, and Industry, there is data that there will be a shortage of up to 790,000 IT human resources in 2030. Under these circumstances, AI (artificial intelligence) and machine learning technologies that can streamline system operations are attracting attention.
<Table of Contents>
What is AIOps?
Why AIOps are attracting attention
Main advantages of introducing AIOps
・Prevents human error and improves productivity by alert prediction,
reduces maintenance and operation costs, reduces resources, and enables real-time data aggregation and
Scenes where AIOps are expected to be used
・Performance monitoring ・ Analysis
・Cause identification ・ Analysis
・IT service management
What is AIOps?
AIOps is an abbreviation for Artificial Intelligence for IT Operations, which means “AI (artificial intelligence) for operating IT”. It is an operation method that can be expected to improve operation efficiency by learning large-scale and complicated network data with AI and machine learning technology and automating the operation of complicated IT systems.
AI/machine learning technology can support the system operation work that has been done manually so far, and it will be possible to operate it more accurately and efficiently. Machine learning is a technique that can derive correct results by learning patterns and rules from a huge amount of data.
For example, for “Recommended products for you” that appear on the EC site, analyze past browsing, purchase history, and patterns with people who purchased the same product, and automatically propose products that you may be interested in. I am. This is also an AI technology. In the conventional way of selling products, the clerk remembers the customer’s interests and tastes and makes proposals, but AI automates this.
In this way, AI Ops utilizes the technology that supports the work that has been carried out by humans for the operation of IT systems.
Why AIOps are attracting attention
The reason why AIOps is attracting attention is due to the shortage of engineers mentioned at the beginning. Nowadays, there is an urgent need to promote DX and digitize companies, and systematization is progressing in all operations.
As a result, the workload of system operation becomes heavier, and there are increasing cases where valuable engineer resources and costs must be devoted to operational work. In addition, since a huge amount of data is output from all networks and systems, the burden on engineers who analyze and analyze it is also heavy.
According to a report released by Gartner, only 5% of companies were using AIOps as of 2017, but by 2022, 40% of major companies will combine big data and machine learning. We anticipate that we will support and improve the efficiency of various operations such as operation monitoring and service desks. In other words, we anticipate that the number of companies that carry out operational operations that utilize AI Ops will increase significantly.
In the future, it will be important and indispensable for the growth of a company to minimize the costs and resources required for system operation. As a means to overcome these problems, AI Ops, which automates operational operations and supports system operations, has attracted attention.
Main benefits of introducing AI Ops
In this chapter, we will explain in detail what kind of benefits can be obtained by introducing AI Ops. This time, I would like to introduce three major advantages.
Prevent human error and improve productivity with alert prediction
The first advantage is that human error can be prevented by stable automation and support by AI. Performance is not stable with human hands due to physical condition and environment, but with AI, performance can be kept constant.
In addition, AI analyzes and learns the alerts that have occurred and how to deal with them, so that alerts that do not need to be dealt with and serious alerts can be automatically separated, and alerts can be predicted and the alerts can be dealt with in advance to reduce the risk of system down. You can avoid it. As a result, it is possible to reduce the time required to process problems and increase the reliability and productivity of operational operations. As a result, it can be expected to lead to improved customer satisfaction.
Reduction of maintenance and operation costs and resources
AI Ops, which automatically performs the cause, analysis, and repair of alerts, can save and reduce valuable engineer resources. In addition, if simple tasks such as basic alert response can be automated, there will be a great advantage in terms of costs associated with operation.
This allows us to focus on important tasks that only humans can do, such as making important business decisions and building relationships of trust with our clients.
Real-time data aggregation and analysis are possible
With the advancement of digitization of operations accompanying the company’s DX strategy, huge amounts of logs and data are output from different sources such as servers, clouds, network devices, firewalls, and other security devices.
It is very difficult for humans to aggregate and analyze these vast amounts of data. That is why by introducing AI Ops and realizing a series of flows that automatically shape and analyze the huge amount of accumulated data, real-time data analysis becomes possible. Real-time data analysis is indispensable even in businesses that need the ability to flexibly respond to major changes.
Scenes where AI Ops is expected to be used
I’ve explained that there are many benefits to using AIOps, but you may be wondering where you can use them.
This time, I would like to introduce four specific situations where AI Ops can be expected to be used.
At maintenance and operation monitoring sites, devices constantly send large amounts of data such as alerts and event logs. An infrastructure environment that emphasizes fault tolerance and flexibility requires the introduction of complex networks and many devices, which places a heavy burden on monitoring operations.
AIOps automatically performs high-level monitoring, analysis, and maintenance work for various data formats, huge amounts of data, and complex infrastructure environments with advanced machine learning functions, and keeps system performance normal. Support is available.
In the past, anomaly detection required human judgment. This is because the “abnormality criteria” differ depending on the conditions. If a large number of alerts are detected, you should check them all.
AIOps can be expected to be used for anomaly detection of this system. Not only major abnormalities such as a link down and system down but also KPIs are learned by comparing and analyzing with past data and scores, so values that exceed the threshold under certain conditions are judged as “abnormal” and detected. It is possible.
Identification and analysis of the cause
When an event or alert is detected, it is not possible to take appropriate action unless the cause is identified and analyzed. Therefore, a lot of man-hours and resources were required to identify and analyze the cause.
AI Ops allows you to analyze the root cause by machine learning and identify how to solve it. In addition, by analyzing the cause, it is possible to determine whether countermeasures are necessary or not, which greatly contributes to the reduction of operating costs.
IT service management
To provide appropriate IT services to users, AIOps is also an effective means for IT service management (ITSM) that manages from IT service design to support.
By accurately predicting device management such as performance management and storage management based on big data, it is possible to appropriately implement trends in usage conditions and problem-solving.
In the past, when an alert was generated from one device, it was necessary to manually acquire information on other related devices and analyze the cause. This is because it was not possible to respond without human judgment as to which device information was required depending on the content and timing of the alert.
AI Ops makes it possible to automate these decisions. By acquiring and analyzing logs and events from the equipment required for an incident that has occurred, it is possible to determine whether or not a response is necessary.
In this article, after giving an overview of what AIOps is, we introduced the merits and specific usage scenes.
The introduction of AIOps can also be started with a small start. Rather than deploying to a company-wide system from the beginning, it can be phased in from a specific department or one system. To get the many benefits of AIOps, why not consider introducing it from a small start first.
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