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End-user monitoring gets a leg up with AI, analytics
This article is part of the Access issue of November/December 2017, Vol. 1, No. 8
As more endpoints and applications make their way into the workplace and generate more data, traditional monitoring tools -- and the IT professionals who use them -- struggle to keep up. It's harder to identify the causes of application and infrastructure performance problems and respond to them in a timely manner. In response to these challenges, new end-user computing (EUC) monitoring tools that rely on AI, machine learning and big data analytics have emerged. These technologies are popular buzzwords throughout the IT industry, and they may conjure up visions of a future in which sentient robots rule the world. In the EUC market, however, they're real, and their time is now. "There is a lot of hype around it," said Jarian Gibson, an independent EUC consultant. "But I understand why, because it's where these tools need to go." Three end-user monitoring challenges Infrastructure and application monitoring software typically looks for deviations in normal behavior, such as spikes in an application's CPU or bandwidth use. It then ...
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