What Is IT Event Grouping?

What is an IT event group?

An IT event group is an association of related events. After a user runs an initial search of event data collected by a cloud monitoring tool, they can group the results into event patterns to display a smaller subset of events that share characteristics. Each of these events can then be classified as a particular type of event, and all of them can be grouped into a single event.

Consolidating an event into the event group around the same issue is critical for correlating cloud infrastructure data to quickly identify and resolve problems in the environment.

How does IT event grouping work?

IT event grouping works by using algorithms and machine learning to sort and group similar events together, which are indexed by cloud performance monitoring tools. Users can search for specific types of events and classify them using a categorization system called “event types,” which let you sift through large amounts of data to identify related events.

For example, if you save a particular search as an event type named “successful_purchase,” any event returned by that search gets “eventtype=successful_purchase” added to it at search time.

Related events then can be grouped into a single event called a transaction. Transactions can include different events from the same source and the same host, different events from different sources from the same host, or similar events from different hosts and different sources.

Transactions returned from a search consist of the raw text of each event, the shared event types and the field values. For example, a user may run a search that groups together all of the web pages a single user or client IP address looked at over a specific period. This search takes events from the access logs and creates a transaction from events that share the same client IP value that occurred within five minutes of each other within a three-hour time span: “sourcetype=access_combined | transaction clientip maxpause=5m maxspan=3h.”

How are event groups used to correlate events?

Event groups make it easier to correlate machine data produced by a cloud environment in an effort to troubleshoot system and service problems. This is important because cloud IT infrastructures produce enormous volumes of data in a variety of formats that are challenging to analyze.

Event grouping is part of a monitoring technique called IT event correlation, enabled by ITSI tools called event correlators. Monitoring data gathered across the environment is automatically fed into the correlator. Machine learning algorithms analyze the data, identify similarities and consolidate it into groups around the same issue. These groups are then compared to data about system changes and network topology to uncover the root cause of performance problems and their solutions.

Event correlation processes event data in the following steps:

• Aggregation: The correlator ingests a stream of monitoring data from various devices, applications, monitoring tools and trouble ticket systems.
• Filtering: Events are filtered by user-defined criteria such as source, time frame or event level. This step may alternately be performed before aggregation.
• Deduplication: The correlator identifies multiple events triggered by the same issue. For example, 100 people may each receive the same error message, which would generate 100 separate alerts. Despite multiple alerts, there is usually only a single issue to address. Deduplication can make that clear to IT teams.
• Normalization: Monitoring data from different tools often uses different terminology for affected components — “server” vs. “host,” for example. The correlator converts all data to a uniform format so the machine learning algorithm can interpret it all the same way, regardless of the source.
• Root cause analysis: In this step, the tool analyzes interdependencies to determine the root cause of a problem. For example, the event correlation tool examines an event on one device and determines its impact on each device in the network based on its knowledge of the network topology.

Through the process of event correlation, event grouping helps organize IT events for easier infrastructure management, authentication, troubleshooting and optimization. Most tools allow users to correlate different types of events into the following categories:

• System events: These describe anomalous changes in system resources or health, such as a high CPU load or a full disk, for example.
• Network events: Indicative of the health and performance of switches, routers, ports and other network components, these events can also be generated by network traffic if it falls out of defined thresholds.
• Operating system events: Generated by operating systems, such as Windows, Linux, Android and iOS, these events describe changes in the interface between hardware and software.
• Database events: These events relate to the reading, storing and updating of data in databases.
• Application events: These events are generated by software applications and can provide insight into application performance.
• Web server events: These describe hardware and software activities that deliver web page content.
• User events: These types of events indicate infrastructure performance from the perspective of the user, such as number of downloads or site visits, and are generated by synthetic monitoring or real user monitoring (RUM).

How do you view patterns in IT event grouping?

You can easily view IT event grouping patterns and event details by performing event pattern analysis in your ITSI tool, often by using a specific search string. You can then use event pattern analysis to see the most common kinds of events in that dataset and create event lists.

Event correlation tools usually include a pattern identification function as part of their user interface. Clicking on a Patterns function or tab, for example, would trigger a secondary search on a subset of the current search results, with each pattern representing a set of events that share a similar structure. You can click on a pattern to:

• View the approximate number of events in your results that fit the pattern.
• View the search that returns events with this pattern.
• Save the pattern search as an event type and create a group name, if possible. Not all event patterns can be saved as event types.
• Create an alert based on the pattern (e.g., alerts that trigger when certain patterns increase or decrease in frequency.)

How do you monitor IT event groups?

A group of events is monitored with an ITSI solution. These software tools employ artificial intelligence (AI) and machine learning that apply grouping algorithms to help IT managers and administrators monitor complex cloud environments with the primary goal of predicting and preventing service disruptions.

ITSI tools collect and analyze event logs from across cloud IT environments. Machine learning algorithms process the data to identify patterns and trends in network activity that could result in service degradation or downtime. Then ITSI produces alerts to prompt IT teams to take corrective action.

ITSI tools typically follow a four-step process:

• Data collection: The tool gathers data in the form of network events, log files, metrics and other sources from across the network, then aggregates it to provide IT administrators a high-level view of network performance.
• Analysis: Advanced machine learning algorithms process the data to identify and track patterns for each data source.
• Prediction: The algorithms learn what constitutes normal behavior for the various endpoints as they process more and more data, enabling them to predict performance for a given metric and pinpoint the probable causes of service issues before they occur.
• Action: IT teams can use the insights generated by ITSI to proactively correct service issues before they impact users and make sure that agreed-upon service levels are met.

Event grouping and correlation is a core feature of ITSI software. As the ITSI tool ingests infrastructure data in the form of monitoring alerts, it uses machine learning to recognize meaningful patterns and relationships within it. IT teams can use these insights to identify and resolve incidents and outages, ultimately improving the availability and stability of their IT environment.

What are the benefits of IT event grouping?

IT event grouping offers several benefits:

• Reduced “noise”: Event grouping reduces event traffic and noise, providing IT teams with greater visibility into event storms so they can more effectively troubleshoot performance issues.
• More efficient troubleshooting: Sorting, prioritizing and grouping events by incident cuts down on the number of tickets for IT teams, reducing duplicate efforts and enabling responders to focus on resolving the issue.
• Less alert fatigue: Machine learning helps group significant events, creating less of a burden on IT teams to continuously sort through alert noise to find the issues requiring their attention.
• Lower MTTR: Event grouping and correlation provides IT teams with a more comprehensive picture of any given issue, reducing MTTR (mean time to repair), and lowering downtime and its associated costs.

How does IT event grouping support incident response?

An IT event grouping tool uses a real-time machine learning model to identify and create patterns quickly and accurately from the incident data it receives, as well as process and cluster data on each service.

With the exponential rise of data in the enterprise that has increased complexity and expanded scale of systems, IT departments face tougher challenges in designing alerts that convey adequate information for a response or that can effectively correlate various incidents and events. Enormous volumes of data and noise often make it difficult to map dependencies and resulting responses. Consequently, multiple teams often receive notifications for multiple services sourced to just one alert — in turn creating more chaos and unnecessarily funneling personnel and resources away from other critical tasks.

An IT event grouping tool, however, can address these challenges. The algorithm determines which, if any, alerts should be grouped into existing incidents, with the ability to adapt over time to understand new types of alerts as they evolve and corresponding human response behavior. This in turn gives IT analysts and professionals the ability to prioritize the most serious issues, and address and remediate them.

IT event grouping also gives an organization a broader picture of the incidents it regularly deals with, enabling the organization to streamline efforts and develop strategies to tackle the biggest issues over time.

How does efficient IT event grouping boost MTTR?

Efficient IT event grouping boosts MTTR by reducing confusion around and streamlining the investigation of infrastructure performance issues and incidents. IT teams achieve a clearer and more comprehensive picture of their cloud environment, which helps them pinpoint and resolve problems more quickly.

Cloud infrastructures routinely produce huge volumes of events about state changes within the environment, some of which indicate potential or active problems. Traditional IT monitoring tools provide alerts for all of these events but without any context into the root cause or why they are happening, leading to a general atmosphere of confusion. This fragmented and incomplete information can extend to MTTR, potentially resulting in prolonged downtime and higher costs.

IT event grouping reduces this noise by grouping similar events together, consolidating duplicate events, and focusing on key event groups. This makes it easier for teams to determine which events are relevant and allows them to focus on those that are most significant.

What are the challenges that IT event grouping addresses?

Grouping events addresses a number of common monitoring challenges. Some common ones include:

• Building transactions from multiple data sources that use different field names for the same identifier
• Finding the duration times between events in a transaction
• Finding the latest event for each unique field value, such as the last time each user logged in
• Grouping all events with repeated occurrences of a value to reduce confusion around reports and alerts
• Determining the time between transactions, such as how long it’s been since a user’s visit to your website
• Finding transactions with specific field values
• Finding events before and after another event (e.g. searching for logins by root and then searching backwards up to a minute for unsuccessful root logins as well as forward up to a minute for changes in passwords.)
• Finding events after other events (e.g. you need to get the first three events after a login event, but there is no well-defined ending event)
• Building transactions with multiple fields that change value within the transaction

Fortunately, there are several different ways to group events. Each of these challenges can be solved with specific event grouping “recipes” supported by your ITSI software.

What tools can be used in IT event grouping?

IT event grouping requires the use of cloud performance monitoring tools that can continuously ingest and process infrastructure data. Each of the major cloud providers offers a performance monitoring toolset for its particular platform, as well as accompanying tutorials and informational docs. There are also third-party tools and templates that integrate with multiple cloud service providers. Popular options include Amazon CloudWatch, Microsoft Cloud Monitoring and Google Cloud Monitoring.

How do you get started with IT event grouping?

To get started with IT event grouping, you’ll need a cloud performance monitoring or ITSI tool. Some factors to consider when selecting a solution include:

• User experience: If software isn’t easy to learn, understand and operate, your team won’t use it. A good monitoring solution will have a clean, modern interface with a management console that integrates with your technology infrastructure. Dashboards should be intuitive to navigate and customize. Automation is critical for streamlined and efficient workflow and processes. It’s also important that the native analytics it supports are easy to set up and understand, and that it can integrate with the best third-party analytics.
• Features and functionality: Your event grouping will only be as good as the data fueling it, so be clear on what data sources your tool ingests and in what formats. It’s also important to understand what types of events the tool can correlate (monitoring, observability, changes, etc.), and what steps it takes to process event data (normalization, deduplication, root cause analysis, etc.).
• Machine learning capabilities: You don’t have to be a data scientist, but a basic understanding of machine learning will help you make a better purchasing decision. Machine learning is generally classified as one of two types: supervised and unsupervised. Supervised machine learning uses structured data that includes examples with specific outcomes to guide the algorithm, which is essentially “trained” using existing data to predict the outcome of new data. Conversely, unsupervised machine learning “explores” unstructured data without any reference to specific outcomes, enabling it to identify patterns and cluster them according to their similarities. Cloud environments generate both structured and unstructured machine data, so it’s critical that a monitoring solution supports both types
• Integration with your tech stack: It’s important to know if the cloud monitoring tool you’re considering can integrate with your vendor partners and other tools and widgets in your environment so you can ensure comprehensive visibility.

Complex cloud environments produce an unwieldy amount of data, and traditional IT tools don’t provide the necessary context to make sense of it. Event grouping helps IT teams see through the storm by reducing the noise and surfacing the most critical issues that require attention. It is an essential technique for effective performance management and for providing your customers with the high-service availability they expect.