From Chaos to Clarity: AIOps, MTTR, and the Road to Resilient Operations

From Chaos to Clarity: AIOps, MTTR, and the Road to Resilient Operations 

In today’s hybrid IT environments, alert storms feel like a commonplace ocurrence. While serving their purpose of notifying ITOps teams of potentially urgent business-impacting issues, they can also create stress and fatigue as teams engage in what can feel like an endless fire drill, constantly switching between siloed monitoring tools to identify and resolve the root cause of software incidents. 

Navigating this chaos requires efficient log analysis, to catch errors, anomalies and other events that can help diagnose problems. A crucial part of troubleshooting software anomalies, logs are: 

• Ubiquitous: All software is instrumented with log messages to aid developers in debugging potential issues, eliminating the need to add code to create custom events or traces. 
• Reliable: When a log event is generated, ITOps teams can immediately discern which part of the software generated it, making logs a reliable indicator of root cause. 
• Human readable: Developers annotate log events using keywords or phrases, making it relatively easy for an unskilled engineer to extract keywords and understand key log events, enabling the correlation of log groups with information stored in ticketing systems, bug databases or even internet resources for a better understanding of the full IT ecosystem. 

Despite their benefits, analyzing software logs, even for experienced engineers, is a highly manual process that can limit insights, increase mean-time-to-repair (MTTR) and hinder resilient operations.  

The Burden of Manual Log Analysis 

Today’s distributed SaaS applications feature hundreds of microservices, generating billions of log entries daily. Navigating this vast sea of logs, spanning numerous services and encompassing an ever-expanding array of failure scenarios, can be daunting.  

For the most part, logs are noisy and unstructured, making anomaly detection a challenge. Engineers must sift through millions of log events in search of unusual spikes or bad events (errors, alerts, warnings, etc.). However, these events merely serve as symptoms rather than identifying the cause of an IT incident.  

Once the symptoms are identified, engineers must scan the logs looking for novel, new or unusual events, such as new deployments, upgrades or configuration changes. Relying on their expertise, engineers must then infer connections between the new event and downstream errors. Finally they end up searching the public domain for instances of similar events to increase confidence in their findings. 

This brute force approach to log management is incredibly time-consuming and impractical to scale across complex hybrid IT estates.  

Consequences can include delayed MTTR, service downtime, and adverse customer and business impacts. Furthermore, engineers find themselves mired in analysis, unable to concentrate on initiatives that propel the business forward.  

Enter Log Management (and Its Accompanying Shortcomings) 

To address this challenge, traditional log management tools have vied for leadership in areas such as cost, scalability and speed of search. By creating rules that monitor logs for specific events, event details or event patterns, these tools help automate the process of identifying and understanding certain software issues.  

This method of log management can prove very effective in relatively static or simple environments, where engineers can build experience (and rules) regarding diagnostic events that indicate problems. However, it also leaves a large visibility gap surrounding unknown or novel incident causes or symptoms, making identifying root cause challenging and difficult to scale as the environment gets more dynamic, and complexity increases.   

Integrating AIOps into Log Management 

A better approach to log management is one based on machine learning (ML) and artificial intelligence (AI), rather than a brute force approach entire reliant on human drudgery. ML models can be trained, like a very experienced human, to find unusual events and correlated patterns in logs. But, unlike humans, ML has the capabilities to scale with complexity, while analyzing data faster than human eyeballs. 

The  Zebrium AI Log Analysis tool embedded into ScienceLogic’s SL1 platform uses unsupervised ML to automatically find the root cause of software problems by uncovering clusters of correlated anomalies and errors across millions of log streams, effectively analyzing and understanding the log environment without manually monitoring and management.  

SL1 autonomously observes the hybrid IT environment, looking for problems and swiftly delivering highly accurate results in near real-time. The tool can diagnose root cause 10x faster and with 95% accuracy which means much faster MTTR and more efficient operations. 

And, unlike supervised ML, which requires time to learn and train for accuracy, delaying time to value, with SL1, no painstaking supervised training is required. The unsupervised ML is capable of producing accurate predictions in less than 24 hours. 

Achieving More Human-Friendly Log Insights 

One of SL1’s key capabilities is simplifying the jobs of ITOps teams and engineers who routinely troubleshoot problems using logs. 

SL1 identifies the root cause of incidents – even unknown unknowns – and produces digestible root cause summaries based on a natural language model with generative AI. These reports visualize the timeline and specifics of the problem, highlighting the root cause, most severe symptoms and other pertinent events in the sequence.  

This is a significant time saver for engineers, distilling billions of logs into human-friendly summaries with a handful of events and plain english explanations, so issues can be understood at a glance, without the need to jump between disparate monitoring tools. Rather than telling engineers what to look for, SL1 shows them what to look at in order to remediate incidents. This means that Level 1 and 2 engineers can tackle troubleshooting tasks that normally involve the intervention of Level 3 engineers.  

The Journey’s End: Resilient, Autonomous Operations  

The integration of AI and ML-driven log management significantly enhances enterprise resilience by automating root cause identification, in turn expediting MTTR and bringing clarity to chaos. Working alongside ITOps teams, SL1 provides recommended remediation actions to enhance their understanding of potential issues, their origins and how to fix them. These recommendations ensure that organizations can effectively tackle evolving hybrid IT challenges with agility, efficiency and at scale. And, when organizations are ready, these existing capabilities can empower them to establish a self-empowered “Autonomic IT” operational environment in which events-driven automation initiates proactive problem detection, troubleshooting, and automatically triggers remediation commands and other automations to fix issues faster – enabling delivery of even more business value.  

SL1 can bring order to your log chaos – see it in action by taking a product tour or requesting a demo today.