Log analysis is an integral part of any software development lifecycle's ultimate success. Developers and engineers use logs to assess what is happening at every layer of a software system and track down the root cause of issues. As the software development process produces a large amount of distributed log data, it is often difficult to analyze it all sufficiently.
Machine Learning (ML) and AI are garnering a lot of attention for their application in analyzing logs. ML tools and algorithms can ease the painful process of anomaly detection so that developers can focus on ensuring high quality and utmost performance in the software product.
This article will discuss the current methods for training AI and ML models for log analysis and anomaly detection. It will further attempt to present other approaches for ML-driven log analysis that are gaining prevalence in the world of software development.
Several ML and AI tools can help the monitoring, collection, and evaluation of logs in a centralized location. The accumulation of system-level insights from log data collected in this way can allow developers to rapidly find and fix issues. This was well demonstrated in the recent creation of a smart anomaly detection system for oil and gas turbomachinery by Daffodil.
Most of the approaches for ML-based log analysis usually fall into one of the following categories:
1)Generalized Algorithms
These ML algorithms are designed in such a way that they parse string-based data to detect anomalous patterns in them. The most popular string-based data parsing algorithms include the Linear Support Vector Machines (LSVM) and the Random Forest algorithms.
SVM categorizes the probabilities of certain words in log lines, correlating them with various incidents. Strings like 'wrong' or 'failure' may trigger an incident and receive a high probability score in anomaly detection. Both SVM and Random Forest evaluate the combined score to detect an issue and require a lot of data to present accurate predictions.
2)Deep Learning
Deep learning, where neural networks are trained on large volumes of data, is generally combined with supervised learning approaches. The combination of these algorithms is applied for complex tasks such as image and speech recognition quite effectively. The application of ML to parse logs into event types further improves the accuracy of log anomaly detection.
This approach involves having to go through large volumes of data to build accuracy over time. New environments would take longer to serve accurate predictions because of this and smaller environments may not be able to accommodate the required volumes of data.
Many ML experts and data scientists use expensive bare metal components to train their models quickly for anomaly detection. In addition to increasing the overall cost, traditional ML approaches also present some of the following challenges:
The increasing scale and complexity of modern software systems have been leading to the expansion of log volumes. Implementing traditional ML techniques for log inspections does not return the most accurate detections. Alerting of critical information and early anomaly detection can only be achieved by the following steps applied as a part of a brand new approach:
1)Structuring, Categorization, and Pattern Learning: Log events are structured and categorized by type using unsupervised learning. Based on the number of event type examples identified, multiple ML approaches are applied. The model then continues to get more accurate and learns to adapt to changing event schemas. The foundation for accurate ML-based log analysis is formed by learning the patterns for each event type.
2)Anomaly Scoring: Each new event is parsed and scored on the basis of how anomalous it is. The rarity and the severity of events are the two main factors that go into anomaly scoring. Following this, the categorization of events becomes very accurate. Despite this improved ability to detect anomalies accurately, the log anomalies themselves can be very noisy.
3)Correlated Clusters: The ML algorithm examines log streams to look for concentrated collections of correlated anomalies. This ensures that there is no coincidental or accidental occurrence of random anomalies in logs. The uncovered patterns are then assessed to form the basis of a root cause report. This would contain both root cause log line indicators and anomaly symptoms.
4)Metric Anomalies Identification: If used in the right manner, ML algorithms can collect instrumented metrics and can identify outlier metric values or metric anomalies. To justify what is found in the logs, the metric anomalies must coincide with them. No human intervention is required to classify which metrics might be useful when troubleshooting a problem.
5)Solving Last Mile Problem: The details of a software issue must then be broken down to a minimum number of log lines. This is passed to an AI model which then returns a value that is passed on for root cause summarization. The summaries that result from this are very useful for resolving software anomalies.
6)Root Cause Report: The log lines generated in earlier steps are combined to form the root cause report. The metrics anomalies in the report are shown as charts. A webhook can be used to deliver the report to another application such as an incident response tool or viewed interactively.
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With AI and ML algorithms and other analytical tools, log analysis lets you find the root cause of issues in your software product automatically. The collection and analysis of logs is usually the biggest bottleneck in the software incident response process.
Discover Daffodil's AI Application Development expertise to automate anomaly detection and resolution so that you can optimize your software development lifecycle.