How Does Machine Learning Strengthen Incident Management?

Jun 10, 2022
3 min read

Automated and adaptable systems are the result of digitalization, artificial intelligence, and machine learning. IT and DevOps teams find it difficult to troubleshoot and diagnose problems with these systems, even though they are important for business success.

These "smart," data-driven applications are now at the heart of company operations, and the financial repercussions of any system failure have increased. Machine learning (ML) has become more necessary to manage and debug today's IT infrastructure, which is both understandable and counterintuitive.

Automation and AIOps for Incident Management

AIOps, a process in which AI is applied to a wide range of IT operations jobs, includes machine learning for incident management.

Event correlation, analysis, and incident management are all areas where data analytics and ML modelling can drastically shorten the time necessary to diagnose and fix problems when applied to an aggregated repository of system, security, and application data. It also increases the quality of incident response output by incorporating subject matter expertise and sophisticated mathematical approaches into machine learning-augmented IT support software.

Management systems for machine learning incidents based on structured and unstructured data

Constraints in Data Modelling

Various approaches are needed because of the extensive range of reasons for a service or application outage. Configuration changes, software updates or patches, equipment failures, external network congestion, or malicious assaults, such as distributed denials of service, data corruption, or system hacks, are among the possible causes of outages.

There are a number of common methods for these situations:

  • Clustering and correlation of data. To connect the dots between two or more related events. Consider a network failure caused by faulty routing information following a configuration change.
  • Detection of anomalies. Detecting any deviations from the regular patterns or continuity of data streams.
  • Fitting and forecasting data. Using a variety of statistical techniques, both old and modern.
  • Intensive training. Using neural networks trained on previously categorized data to assess fresh data streams.

Among the machine learning models used by incident management software are the following:

  • A z-score, for instance, or a t-score;
  • Analysis of linear and logistic regressions
  • With data with non-normal distributions, generalized linear models are an extension of classic regression approaches that incorporate techniques such as ANOVA (analysis of variance);
  • Time-series forecasts can be made using the Auto-Regressive Integrated Moving Average, or ARIMA.
  • Classification and pattern recognition using support vector machines
  • For anomaly detection, a local outlier factor
  • Identification of anomalies by elliptic envelope
  • A powerful predictive model but a computationally costly one: Gradient boosting machines
  • The random forest and clustering for anomaly detection.
  • Nearest neighbor grouping and anomaly detection using K-NN and K-means
  • Auto-encoders;
  • In-depth study;
  • A technique is known as "transfer learning," in which trained models are applied to new datasets.

Using Supervised Learning to Find a Problem

Many machine learning-enhanced incident management systems begin with techniques akin to rules-based AI prevalent in the 1980s for identifying and classifying problems.

In recent years, a posteriori data-based systems have replaced a priori techniques, which were based on facts rather than experience. These systems use ML modeling and the massive amounts of system, event, and performance data generated in today's data centers. If a new configuration change was the cause of an occurrence, an incident management system driven by machine learning may utilize a classification model trained on the historical incident database to make the prediction.

AI-enhanced Automation is the Future

Incident management software that incorporates machine learning can support multiple levels of automation that are similar to the categories outlined for self-driving vehicles, namely:

0. There is no automation. The IT staff is responsible for all of the processes.

1. Administrative support By filtering data, like important events and warnings, the system detects probable causes and proposes remedies.

2. Automation of some or all of the processes. Unattended system reboots and power cycles, for example, or the execution of a script to complete a previously manual workflow, are examples of common problems that systems fix.

3. Automated systems that are only activated when necessary. Automated workflows are used to apply hotfixes and correct more complex issues without the need for human intervention.

4. Automation to the hilt Preventative measures, such as configuration changes and software upgrades, is used to resolve issues such as resource restrictions or component failures before they become problems in the first place. There are still many years before fully automated systems are a reality for AIOps vendors, notwithstanding their aspirations.

Benefits and Dangers to IT Managers

Automated incident management software improves the ability of less experienced administrators to resolve incidents, reduces the time it takes to resolve incidents, aids in post-incident review and root-cause analysis, and lowers the overall stress on operations center teams that monitor hundreds of systems, each streaming gigabytes of data per minute.

Machine learning models can be constructed incorrectly, tweaked incorrectly, and applied indiscriminately in any automated system. This is the greatest risk. Automation runs amok by artificial intelligence (AI) could bombard operations employees with alarms—called noise at such a level—misidentify underlying causes and deploy insufficient or wrong fixes or configuration changes in the worst-case scenario.

AIOps and incident management systems infused with machine learning should be tested in low-risk scenarios before being gradually deployed to major production systems for the same reason aviation autopilot systems must undergo thorough and lengthy testing.