Automated Root Cause Analysis: Finding Diamonds in Mountains of Logs
We’ve all been there at one time or another in our careers: a business-critical service failed, and the emergency recovery clock starts ticking with a vengeance. Not just ticking; it’s a blaring siren and a firehose of inquiry from concerned application owners and executives. Worse, both your customers and your team are frustrated. Customers because an app they depend on has brought them to a halt, and your team because resources are diverted to fix the crisis. And perhaps the most perennial of troubleshooting resource drains during application outages is the manual analysis of dozens or hundreds of logs, and millions or billions of messages. It would be easy to think that by now ops teams would have access to powerful analytic tools to make quick work of automating root cause discovery. To be fair, both vendors and open-source have delivered log aggregation and query platforms that at least simplify the first-order log problem- making log data easier to access. But they still require admins with talent and deep understanding of applications to spot a never-ending list of novel failures deep within application frameworks. Fortunately, machine learning and artificial intelligence are now being combined to assist operators in quickly identifying the root cause of issues and begin resolution right away. ScienceLogic Zebrium AI Log Analysis is a great example. Automated Analysis Begins With Automated Learning Step one in supercharging incident response is the automatic ingestion and processing of millions or even billions of log messages in real-time. However, that function must be truly automatic. Overloaded human admins do not have time to train yet another tool. ScienceLogic Zebrium AI Log Analysis automatically learns how to understand log messages, including what data is significant, which messages are unusual, which are noisy, and even how to decode the details in previously unseen log formats. This unsupervised machine learning typically begins delivering results within 24 hours of exposure to new logs. Better, it can result in a tenfold faster resolution process. Untangling Unknown Unknowns Modern applications are complex, and the novel nature of many errors makes understanding what broke a daunting task. This is why logs remain the gold standard for troubleshooting issues. Well-understood failure modes send alerts or event messages clearly indicating the issue and providing context for repair. However, most critical outages result from issues never previously encountered, and the only evidence might be an obscure, single message among millions of lines of noise. Zebrium correlates unusual behavior with recent changes and performance metrics, helping you understand potential business service impacts before they become full-blown incidents. Fluent Klingon Not Required You're not alone if reading logs feels like deciphering a foreign language. Each log has its own unique syntax and vocabulary, making troubleshooting challenging. That challenge is multiplied for each new log that must be manually investigated. Zebrium AI Log Analysis automatically translates arcane formats and fragmented details into plain language that’s easy for the whole team to understand, naturally. Going beyond identifying which log lines are related to the cause of issues, Zebrium’s AI engine explains issue details in plain language. Its natural language model goes further to generate root cause summaries that describe the systems involved and the relationships between application elements. It also visualizes the most critical keywords from related log messages. When teams immediately recognize application details, they can trust the accuracy of automated analysis. Ready, Set, Analyze! If you’re ready to transform your log analysis and incident resolution process or are simply curious about how automated root cause analysis might streamline your troubleshooting effectiveness, you can request a free trial of ScienceLogic Zebrium AI Log Analysis today. It’s SaaS-hosted and easy to get up and running in minutes. Experience the next level of incident troubleshooting today and get back to doing what you love- delivering great service for your customers.Introducing our newest Blogger: Gavin Cohen VP of AI Product Management
Hello Nexus Community Members and ScienceLogic Customers, I wanted to take a minute to introduce our newest blogger who will be keeping us up to date on AI Product related announcements, trends and best practices. Gavin Cohen is VP of AI Product Management at ScienceLogic and has over 20 years’ experience across a diverse range of technology roles. At ScienceLogic, he is responsible for defining the company’s AI/ML product roadmap and strategy, working closely with customers, partners and internal teams. Gavin joined ScienceLogic through the acquisition of Zebrium, where he was VP of Product and Marketing and part of the founding team. Prior to joining Zebrium, he was VP of Product and Solutions Marketing at Nimble Storage where he redefined the company’s category and positioning leading to a successful acquisition by HPE. He has also held senior product management, business development and technical evangelist roles in Australia and the U.S. Gavin has a Bachelor of Computer Science and an MBA.Introducing our newest Blogger: Joshua Ellsworth- Sr. Sales Engineer
Hello Nexus Community Members and ScienceLogic Customers, I wanted to take a minute to introduce our newest blogger Joshua Ellsworth- Sr. Sales Engineer. Joshua Ellsworth is a Senior Sales Engineer primarily working with Enterprise customers at ScienceLogic. In his eleven years with the company, he filled roles in post-sales professional services and sales operations prior to moving to the pre-sales. He has a wide-ranging background in IT from end user support to systems administration and web development, and has experience programming with python, php, and javascript. Fun fact: Josh has a Master’s Degree in Library and Information Science.
