2022-07-12Once upon a time, development and operation and maintenance "love each other and kill each other". Now, with the emergence of the "DevOps" strategy, the boundaries between development and operation and maintenance have become blurred, and because of this, new applications and new products appear on the market faster and faster, and the QA process is becoming more and more complex. Shorter, this is the biggest improvement brought about by the automation trend.
In this era of relying on speed to win, the demand for automation has become more urgent. Many DevOps teams realize that AIOps (intelligent operation and maintenance) is of great significance for improving the ecology of IT management systems and improving operational productivity.
With the popularity of hybrid and multi-cloud architectures, as well as the rapid development of edge and IoT devices, more and more enterprises use some hybrid models or cloud infrastructure for project development. As a result, the IT system architecture has become more complex than before, and the IT team must adapt and keep up with the pace of change, and ensure the reliability of applications and services.
For operation and maintenance personnel, the most frightening thing is not the failure, but the failure to find the reason. In the DevOps team, if you focus on infrastructure construction and operation and maintenance, it will slow down the productivity and agility of the entire team.
If DevOps is to expand technology by manpower, then "AIOps" emphasizes the expansion of technology by technology. To put it simply, AIOps is based on existing operation and maintenance data, such as logs, monitoring information, application information, etc., and further solves problems that cannot be solved by automated operation and maintenance through machine learning. Freed from error-prone processes.
An ideal AIOps platform needs to have the following capabilities:
1. Possess full-line real-time data collection, algorithm analysis and cross-system tracking capabilities, efficiently integrate IT data, and realize real-time analysis of failure causes.
2. Set a dynamic baseline to capture abnormalities that exceed static thresholds, realize multi-index anomaly detection and eliminate false positives and redundant events.
3. Predict potential failure risks based on intelligent algorithms and machine learning, and provide timely solutions to existing failure events.
With the intelligent AIOps operation and maintenance platform, the DevOps team can access massive data from different business, management, and monitoring systems, and conduct rapid analysis through intelligent algorithms, so that tasks that originally took hours to complete can be completed within seconds, and even predict potential application problems and equipment failures.
Since Gartner proposed AIOps in 2016, in just a few years, AIOps has completed the transformation from technical concept to marketization, and more and more enterprises have begun to use AIOps to improve operational efficiency and expand business space. On the road of automation exploration, Dell Technologies has also promoted automation transformation through a series of digital initiatives.
Through the development of the DevOps transformation movement, developers at Dell Technologies have been able to spend 70% to 75% of their working time on writing code and gain more than 35% productivity improvement. Now, Dell Technologies is leveraging AIOps to accelerate DevOps team velocity and achieve positive business outcomes.
"CloudIQ" is an AIOps active monitoring and predictive analysis application launched by Dell Technologies. Its functions cover servers, storage, data protection, storage areas, networks, hyper-converged and converged infrastructure, and can help operation and maintenance personnel deeply understand the foundation of Dell Technologies. structure to improve the efficiency of operation and maintenance management.
