Shulou(Shulou.com)06/03 Report--

Gartner predicts that by the end of 2019, a quarter of enterprises worldwide will strategically implement AIOps to support two or more IT operations functions. By 2022, 40% of large enterprises will combine big data and machine learning capabilities to support and partially replace monitoring, service desk, and automated processes and tasks.

Leveraging AIOps for IT operations is coming right now. Where is your company currently located?

After all, the ultimate goal of AIOps solutions is to make engineers 'lives better, and that's what all IT people are committed to, and it's worth it.

Business performance scenarios lead to rapid development of AIOps

From the following figure, it can be seen that AIOps capabilities can be developed in several monitoring areas such as DEM, ITM, NPMD, APM, etc. AIOps needs to be combined with AI technologies such as machine learning and deep learning to play a greater role in scenarios with a large amount of data, and the scenario of business performance monitoring is satisfied with this. This is the source of market demand for Huaqing Rongtian to use AI technology to study business performance monitoring earlier.

Huaqing Rongtian's business performance monitoring products are innovative to some extent. They obtain network traffic packets in real time through bypass mirroring, and obtain a large amount of dynamic business data after protocol analysis, especially in financial, tax, securities and other scenarios.

This service performance monitoring is a great extension and innovation to application performance monitoring (APM) defined by Gartner. They are implemented in different technical ways. Huaqing Rongtian service performance monitoring products collect data through bypass mirroring, while traditional APM collects data more by dialing or agent.

Although the technical implementation varies, the goal remains to ensure the availability of the application system and improve the user experience. Foreign manufacturers mostly use traditional APM methods to provide integrated solutions such as application performance monitoring, infrastructure monitoring, digital experience management, hybrid Cloud Monitor, etc.

However, large domestic enterprises, especially in the production environment, prefer Huaqing Rongtian, which is based on network bypass traffic data acquisition and decoding, to realize real-time monitoring and alarm of application performance indicators, ensuring non-intrusion and zero impact on the application system itself.

Huaqing Rongtian's technical implementation of business performance monitoring has also been recognized by Gartner APM experts in the industry.

Future: operation and maintenance data platform + intelligent analysis brain + automation tools

Enterprise digital transformation puts forward higher requirements for application system operation and maintenance, requiring finer monitoring granularity and more accurate alarm positioning. Business performance monitoring products are being combined with big data, machine learning, cloud computing and other technologies. Future product positioning should focus on the following directions:

(1) Intelligence

Intelligentization has become a necessity for enterprises to apply system monitoring in dynamic and complex IT environments. The types and quantities of data collected by operation and maintenance departments have increased exponentially. It is no longer possible to meet the needs by continuing to rely on manual analysis.

Using massive, real-time, full-stack monitoring data, AI algorithms replace or partially replace traditional manual analysis, give suggestions for problem location and operation and maintenance decisions, shorten MTTR (mean time to failure recovery), and improve the accuracy of analysis and decision through iteration.

(2) Data fusion

Business performance monitoring products need to expand the scope of ingested data types, including network traffic data, log data, application system trace files, and basic monitoring indicators. Through real-time analysis and historical analysis of these data, fine-grained real-time monitoring and full-stack views are realized.

Network traffic data is useful for end-to-end performance monitoring and transaction tracing across business nodes; log data and application trace files help with problem localization and root cause inference; and correlation with underlying monitoring metrics enables analysis of resource availability and performance bottlenecks.

Data fusion and openness include not only the integrity and fusion of ingested data, but also the provision of services to the outside world for various collected data, statistical analysis index data, alarm data, decision suggestions, etc.

(3) Platformization

AIOps is a software platform that combines big data and artificial intelligence or machine learning technologies to enhance and partially replace traditional IT operations processes and tasks, including availability and performance monitoring, event correlation and analytics, IT service management and automation. It provides platform capabilities on which users can implement interactive analysis, as well as out-of-the-box operations tools.

Gartner predicts that over the next five years, a broad range of AIOps platforms will become the dominant form of delivering AIOps capabilities, rather than AIOps capabilities embedded in monitoring tools such as APM, NPMD (Network Performance Management and Diagnostics) or ITIM (IT Infrastructure Monitoring) as they currently do.

(4) Business Value

Business performance monitoring is currently focused on IT operations and application support, while some of the more advanced organizations have begun to use the results of these data analytics to provide insights to business units and leaders.

Operation and maintenance data includes both technical indicators and business indicators, such as transaction volume, transaction type, channel distribution, customer source, transaction amount, etc. These are important data assets of enterprises, which can realize business data visualization, real-time business analysis, business prediction and user behavior analysis.

The data center of enterprises is changing from IT perspective to business perspective, from cost center to profit center, which not only ensures the "stable,""safe" and "reliable" operation of business system, but also helps enterprises improve "customer experience,""operation efficiency" and "business income" through data analysis and service.

To sum up, the future of application performance monitoring products is operation and maintenance data platform + intelligent analysis brain + automation tools, operation and maintenance data platform is the foundation, intelligent analysis brain is the ability, and automation tools are the means.

please look forward to

Huaqing Rongtian business performance monitoring product EZSonar 4 is based on the big data fusion platform. Through data fusion and AI engine, it continuously increases AIOps capability. It has launched dynamic baseline alarm, aggregated dimension alarm, traffic data and log data fusion, real-time data service, business dashboard, WYSIWYG report, platform self-operation and maintenance and other functions, making alarm more effective, problem analysis and location more convenient, data more open, configuration management simpler, Improve product usability and usability.

EZSonar is about to release a new iteration version EZSonar 4.2, which will continue to launch new functions such as intelligent recommendation of alarm analysis results, refinement of alarm objects, alarm consolidation strategy, etc. The features and use scenarios of the above functions will be introduced one after another in the near future. Please stay tuned.

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