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2025-01-18 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Internet Technology >
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Shulou(Shulou.com)06/03 Report--
Cloud Wisdom (Beijing) Technology Co., Ltd. Lu Xinghai
3. His encounter with her (big data and Application performance Management)
It can be said that it is precisely because of years of passing by, in the romantic 2014, big data and application performance management really met. In fact, this encounter is not accidental: on the one hand, it stems from the big data characteristics of application performance management data, on the other hand, it is also because of the higher-level user needs brought about by big data's technological development.
According to cloud wisdom (Cloudwise) to sort out and analyze the data in its application performance management, APM has a very high requirement for real-time data processing. When the performance indicators in the system are abnormal, it is necessary to give an alarm to the user immediately (within 5S). At present, the daily data collection of its products is 72 billion, and it is expected that this data will reach 10 trillion in 2016. These IT data cover 2000 different types of performance parameters, such as user-to-server response time, JS errors, network availability, service throughput, slow queries, system processes and disk space utilization. The data itself contains a lot of value, just like a gold mine is worth us to develop. How to tell the meaning behind the system data to the managers, technicians and operators of the enterprise, so that the company can make effective decisions based on the data, and use visual data to create a better service experience for users-- this is an important problem that needs to be solved in application performance management.
So in what ways can big data technology help application performance management? How can application performance management use big data's concept to help enterprises solve real business and user experience problems? Here is the advance notice, and more content will be discussed in detail in the "value section".
1. Through the visual analysis of IT performance index data, the optimal management of application performance is realized: continuously improve the performance and availability of the application system, so as to continuously improve user satisfaction and ensure business revenue.
2. Collect and mine national and global user experience and performance data, analyze, improve and optimize end-user experience: help enterprises constantly discover and improve real end-user experience problems.
3. Using big data prediction and distributed pressure testing technology to speed up the system development and delivery process: under the miscellaneous market demand and competitive pressure, shorten the iteration time, improve the code development efficiency and quality, and achieve rapid construction and continuous delivery.
4. Establish a business-oriented and ROI-oriented analysis model to avoid and reduce the overall investment risk: help enterprises avoid and reduce the technical and financial risks brought about by the introduction of cloud computing, virtualization and other new technologies in the existing IT infrastructure environment.
5. Performance analysis of deep data mining and refinement to the component level: tracking application problems at all levels in a complex system environment (even going deep into code-level performance bottlenecks), helping IT, development and other departments to improve work efficiency and focus on the core work
[to be continued] (the second "value" and the third "Technology")
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