Riverbed to Showcase Unified APM and End-User Experience Monitoring Solution at Velocity Conference
June 12, 2018
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Riverbed will showcase at O’Reilly Velocity, a conference devoted to web operations, DevOps, and systems engineering, the latest release of Riverbed SteelCentral.

Riverbed SteelCentral blends device-based end user experience, infrastructure, application, and network monitoring to enable customers to holistically manage the users’ digital experience.

This unique approach enables organizations to monitor the digital experience for all users and all applications - on prem, SaaS, and cloud - while capturing every application transaction at a level of depth and quality that is essential for digital business insights. This release also introduces unparalleled “big data” scale for digital business with the ability to collect and analyze full details for billions of transactions per day, and delivers powerful AI and business analytics that provide meaningful insights for IT and the business.

Riverbed Presentation

Title: The “Sound” of Performance Monitoring Data

Date/Time: Thursday June 14, 2018 at 1:15pm - 1:55pm PDT

Location: Room LL20 C

Speaker: Jon Hodgson, Chief Scientist for APM at Riverbed Technology

At the conference, Jon Hodgson, Chief Scientist for APM at Riverbed, will lead a presentation that will explore how the digital media industry leverages fundamental digital signal processing principles to ensure that analog events are captured with precise fidelity, yet in the field of performance monitoring, these principles seem to be largely absent in many tools, which then imprecisely capture the performance data we rely on.

Using sound as a surrogate for monitoring data, Jon will offer an overview of digital signal processing concepts and investigate analogous real-world cases where performance data effectively “lied” to the troubleshooter, suggesting that overloaded resources weren’t and vice versa. Attendees will learn how to avoid these pitfalls and determine if the data from their monitoring tools is sufficient to detect and quantify certain common problems and how to read between the lines to identify when the data can (and cannot) be trusted. Along the way, Jon will explain how other fields have overcome these challenges and how some of their unconventional techniques can be applied to DevOps methodologies.

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