Guide to Everything You Need to Know About AIOps
Get everything you need to know about certain autonomous operations terms.
This Ultimate Guide to AIOps will cover what AIOps is, the components that make up the system, practical examples of AIOps use cases, and questions to consider when searching for an AIOps platform.
What is AIOps?
Put simply, Artificial intelligence for IT operations, also known as AIOps, is the practice of applying analytics and machine learning to big data to automate and improve IT operations.
Gartner coined the term AIOps in 2016, to describe a class of technology that can learn to automate IT operations processes, including event correlation, anomaly detection, and causality determination.
As per Gartner – (source: https://blogs.gartner.com/andrew-lerner/2017/08/09/aiops-platforms/)
AIOps platforms utilize big data, modern machine learning, and other advanced analytics technologies to enhance IT operations (monitoring, automation, and service desk) functions directly and indirectly with proactive, personal, and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies. It leverages the power of AI to automatically analyze massive amounts of network and machine data to find patterns, both to identify the cause of existing problems and to predict and prevent future ones.
Evolution of AIOps
About 7-8 years ago, the IT industry started seeing a lot of complexity being built into the enterprise IT landscape – with many applications, huge growth in compute, and technology innovations fueling an ever-expanding interdependent ecosystem. Also, it led to an explosion of digital data as new technologies got adopted. At the same time, there was a lot of diversity as many legacy technologies, platforms, and applications were still retained.
Traditional IT Operations – which usually consist of a siloed organization having various levels of support teams, command centers, and teams that are separated according to the technology towers became sluggish and reactive. IT Operations teams simply could not keep up with the rate of change in the environment and started incurring high costs of running the entire IT Operations.
There was a huge dependency on a few good people in the organization who were always involved in firefighting efforts during outages. Over the years they would have gathered a lot of tacit knowledge about the infrastructure and when these people left the organization, they created a big void that was difficult for other members of the operations teams to fill. Hence, it became crucial to digitize a lot of this tacit knowledge.
Hence there was an immediate need to reimagine the entire idea of IT Operations and scale up the IT Operations functions to support not just IT Infrastructure but also applications to solve business problems.
All of this created the need of having the construct of AIOps which essentially applies machine learning and AI to vast amounts of ITOps data to breakdown the complexity, remove the silos across technology layers, provide intelligent analytics by correlating observational data as well as engagement data, and use automation to eliminate all the repeatable lower order tasks.
Why Do We Need AIOps?
IT teams play an integral role in enhancing business outcomes – by advancing critical digital transformation projects, delivering optimized user and customer experiences, and ensuring availability.
However, IT Ops today needs to deal with:
- Rising complexity. The modern IT landscape is a mixture of legacy systems, including on-premises mainframes and distributed systems, as well as new technologies, such as containers, cloud, virtual, and software-defined components making it difficult to analyze information across layers.
- Increase alerts. An increased number of monitoring tools for different technologies lead to a huge number of inaccurate and redundant alerts. This complicates operations and increases the time to identify the root cause of issues across systems and domains.
- Dynamic systems. In recent years, the use of containerized applications and microservices has significantly increased complexity due to the dynamic nature of operations.
- Data deluges. The volume, variety, and velocity of data that needs to be managed, correlated, and analyzed continues to grow dramatically.
To deal with such complexities, it is no longer enough to react when issues arise. Teams must gain the visibility needed to identify potential issues—and address them before they affect service levels. To contend with the explosive growth in data, complexity, and user demands, IT teams need to adopt an AIOps platform.
As IT infrastructures evolve, old rules-based systems fall short because they rely on a pre-determined, static representation of a mostly homogeneous, self-contained IT environment.
AIOps uses AI and Intelligent Automation to provide a single-source-of truth for all ITOps processes and detailed root-cause analysis for any IT event, help predict probable incidents, provide intelligent recommendations for fixes, and enable proactive automation to improve the performance of digital services.
Role of AI in AIOps
AIOps rely on the maturity of specific AI models to provide intelligence and visibility to IT operations teams, as well as to provide intelligent resolutions to common IT issues.
Instead of having to rely on IT engineers to identify a problem with an application and fix it manually, AIOps can use algorithms to identify and resolve the problem automatically. Likewise, rather than requiring IT staff to determine how best to manage application performance or how many resources to allocate to it, a platform can provision environments automatically by parsing data to determine the optimal mix of resources.
Algorithms can pick out significant alerts from a noisy event stream, identify correlations between alerts from diverse sources, assemble the correct team of IT specialists to diagnose and resolve a situation, propose probable root causes and practical solutions based on past experiences, and learn from feedback to improve continuously over time.
Clustering and correlation are the most complex and crucial steps, requiring multiple different approaches. A combination of historical pattern-matching and real-time identification helps identify both recurring and net-new issues.
The most advanced AIOps platforms leverage a combination of various types of reasoning to ensure optimal outcomes:
This is also known as the ‘traditional’ approach. It refers to explicit user–defined rules which are required to make a decision.
Case–based reasoning is completely dependent on data and is more adaptive to the changing scenario. The system learns frequent, dominant, and recent cases from historical data and derives patterns. The analysis, predictions, and recommendations based on historical occurrences, frequencies, and relationships are an outcome of “Case-based Reasoning”.
Model-based reasoning is primarily driven by two things – the situational data (CMDB (configuration management database), influencers based on relationship data, inventory list, and so on) and the factual data comprising of technology model or Meta mode. AIOps leverage the structural context and behavioral patterns of the systems and apply reasoning logic for deciding the course of action
Type of Reasoning
What are the building blocks of AIOps?
The 6 Elements of AIOps
- Extensive and Diverse IT Data – AIOps brings together diverse data from both IT operations management and IT service management. This is often referred to as breaking down data silos, bringing data together from disparate tools so they can speak to each other and accelerate root cause identification, and eventually enable automation.
- Aggregated Big Data Platform – At the heart of the platform is big data. As the data is liberated from siloed tools, it needs to be brought together to support next-level analytics. This needs to occur not just offline, as a forensic investigation using historical data, but also in real-time as data is ingested.
- Machine Learning – Big data enables the application of machine learning to analyze vast quantities of diverse data. This is not possible prior to bringing the data together or by manual human effort. Machine learning automates existing manual analytics and enables new analytics of new data all at a scale and speed unavailable without AIOps.
- Observe – This is the evolution of the traditional ITOM domain that integrates development and other non-ITOM data to enable new models of correlation and contextualization. In combination with real-time processing, probable cause identification becomes simultaneous with issue generation.
- Engage – The evolution of the traditional ITSM (Information Technology Service Management) domain includes bidirectional communication with ITOM data to support the above analysis. Artificial intelligence or machine learning expresses itself here in cognitive classification plus routing and intelligence at the user touchpoint. An example of this is a chatbot.
- Act – This is the final mile of the AIOps value chain. Automating analysis, workflow, and documentation is all part of AIOps. Act encompasses the qualification of human domain knowledge into the automation and orchestration of remediation and response.
How does AIOps Work?
Advanced AIOps products should be a blend of Machine Learning, AI, and automation to help drive digital transformation within enterprises and improve their effectiveness and efficiency through autonomous IT Operations.
Here are the main capabilities and activaties of a modern AIOps platform:
1 Enterprise Context
Enterprise systems today lack a comprehensive and unified view of their IT estate that can connect business functions to applications to infrastructure. Most available views within ITOps technologies are often incomplete, inaccurate, and stale.
In the absence of a solid blueprint i.e., a context for IT Operations, the effectiveness of processes inherently relies on intuition and tacit knowledge, making it expensive, time-consuming, of variable quality, and extremely difficult to scale to today’s volumes.
Enterprises need to adopt scalable ways of knowledge engineering to ensure a blueprint is always complete, consistent, and updated. It will empower enterprise IT in many effective ways:
- It will improve end-to-end transparency
- It will enable and accelerate data-driven decisions
- It is vital for cognitive automation at any stage of the IT operations
Once the context is built, the next step would be to profile the data which has been collected. Some of the behaviors which can be profiled are:
- Change: Detect significant persistent changes in the behavior. Could capture changes in mean, variation, patterns, and trend.
- Outlier: Detect outliers in the behavior. Outlier detection is aware of the steady state between changing events.
- Pattern: Detect temporal patterns in the behavior. Patterns include various dimensions such as the day of the week, day of the month, hour of the day, as well as complex dimensions such as the first day of the week, last working day of the month, hour of the day of the week, and so on.
- Trend: Detect trends in the behavior. The trend can be detected on the most recent steady state, or use mined trends and patterns to forecast future behavior.
These insights can be used in different ways to understand an entity’s normal behavior and identify the ones that need attention.
2 Alert and Event Management
In a typical enterprise, the monitoring tools monitor the IT landscape to detect events and alerts and notify them to ITSM or to an event management tool. Following is the brief on events, alerts, and incidents:
Events – Any change that is happening in the IT landscape
Alerts – Specific events of interest that may require action
Incidents – Events that negatively impact the quality of an IT service
Due to this, there is a huge flood of alerts and events going to the command center teams. If there is an outage or critical time for business like the end of quarter etc. the number of these notifications coming in rises exponentially. Many of these alerts could be redundant or repetitive which could lead to a lot of noise in the system making the command center operator miss genuine important alerts. AIOps can be highly effective in this situation due to its various capabilities explained below:
- Alert Detection – Build understanding from the log data, detect events, and perform specific actions example, raising an alert for an actionable event
- Alert Prioritization – When there are multiple process faults (alerts) in the same instance, it becomes hard to decide which ones to solve first and what follows next.
Alert prioritization helps identify the alerts that need immediate attention and/or are critical for the business to maintain efficiency and deal with all faults optimally.
- Alert Suppression – Not all events are important – as some are false positives arising from incorrectly configured thresholds, as well as the presence of duplicate alerts. AIOps can suppress the alert noise and generate just the right alerts at the right time.
- Alert Aggregation – Many symptoms are manifested due to the same event; thereby multiple alerts get generated at various hierarchies for the same event. AIOps products can apply case-based reasoning algorithms to understand temporal correlation patterns between alerts.
- Alert Prediction – Many alerts exhibit a tendency to occur regularly based on external influences of workload and other factors. Based on historical alert data and regular occurrence patterns, AIOps can detect temporal patterns and patterns correlating an alert to other alerts.
- Alert Notification – In cases of actions and event occurrences, an AIOps product can notify the designated user about the details of the events along with the history of related events.
- Alert Dashboards – All the analyses done above with past and current events along with future events can be viewed on a comprehensive dashboard.
3 Incident Management
One of the major benefits of AIOps is that it can autonomously fix incidents without the need for explicit instructions. This process faces a common problem with automation scripts wherein they simply fail on facing an unknown scenario. This requires IT to spend countless hours troubleshooting the automation mechanism itself.
You can create unique automation workflows with your AIOps product for your environment to take on different tasks in your environment. This can be as simple as performing a system reset or as complex as a full end-to-end process that includes validation of completed tasks.
The Selfheal process triggers and applies fixes in an iterative manner. The objectives of an iterative triaging and fix strategy are to:
- Restrict triage to a limited set of influencers and iteratively increase the working set.
- Be right the first time.
- Leverage knowledge of already known fault patterns and fixes either modeled or self learned heuristics
Even with Observability and Analytics, most AIOps fall short of resolutions due to the lack of inbuilt capability to automate actions.
Mature AIOps products can provide out–of–the box capabilities to take actions and provide an ecosystem for other automation tools and scripts and orchestrate across them.
Automated resolutions for incidents are a key capability to ensure continuous operations – leveraging intelligent automation and fixing incidents, as well as taking proactive actions to mitigate any potential, predicted future incident.
Other forms of automation can be in the form of automated health checks across servers and applications to identify an anomaly or irregularity, Automated compliance Checks to verify compliance standards for any Data Center or IT Infrastructure, Automated Life Cycle Operations to manage various change requests and work items that commonly exist in any IT Operation.
5 Proactive Problem Management
Businesses expect their Enterprise IT to be agile, resilient, efficient, be a business enabler, and provide high–quality customer experience. To meet such expectations IT needs to proactively identify problems that could possibly disrupt the services, requiring them to:
- Identify potential risks related to systems, applications, and services that could result in business-critical events. It would bring intelligence in Problem Detection, Problem Resolution, and Problem Orchestration.
- Enable performance and capacity management that can be used for detecting infrastructure problems proactively at Servers, Storage, and Networks. As part of this, a Risk framework can be built which categorizes systems into Healthy, Risky, Possible Risky, and Optimization categories. The problem detection algorithm uses this risk framework to identify the problems for further investigation.
What are the common AIOps use cases?
- Proactive health check – Proactive health checks help organizations keep track of the real-time health of key IT components. It can be configured to check entities or groups of entities and perform the health check within and across technologies for the related set of entities. This is useful in performing ready-for-business or start-of-the-day checks before your business starts each day so that you know everything is up and running as expected, or understand the health of an entity and all dependent entities at a given point in time and display it in a report format.
- Anomaly detection: Anomalies are essentially deviations of key performance indicators from normal or historic values and are useful in identifying potential problems. These outliers are called anomalous events.[Text Wrapping Break]Anomaly detection relies on mature algorithms. A high degree of change of a ￼cohesive group of KPIs, beyond a threshold value (set as per previous patterns), can be picked up by the algorithm, analyzed to check if it signals a potential event, and alerts can be raised for the same.[Text Wrapping Break]
- Event correlation and analysis: Event correlation and analysis is the ability to see through an “event storm” of multiple, related warnings to the underlying cause of events and a determination on how to fix it. The problem with traditional IT tools, however, is that they don’t provide insights into the problem, just a storm of warnings.[Text Wrapping Break][Text Wrapping Break]AIOps uses AI algorithms to automatically group notable events based on their similarity. This reduces the burden on IT teams to manage events continuously and reduces unnecessary (and annoying) event traffic and noise. For example, if two issues arise and administrators can see that one is affecting a payroll service that isn’t being run currently, and another is hitting an e-commerce service that runs 24/7 and accounts for the bulk of the company’s revenues, they can prioritize their efforts accordingly.
What are the benefits of AIOps?
With a cloud-based platform incorporating AIOps capabilities, IT organizations gain the tools and insights they need to move IT from being a cost center to a true partner to the business without having to upgrade and integrate existing IT operations tools and data, deploy a machine learning solution, and add a huge number of additional staff to its IT operations team—all of which would be financially unfeasible.
Gives IT leaders complete, up-to-date visibility across their entire IT operations estate—on-premises and cloud – Because fostering and maintaining the business’s trust in IT is one of the IT organization’s biggest concerns, it’s critical for the IT organization to be able to keep the systems and applications the business depends on up and running.
Enables IT to proactively identify service health issues, and then quickly pinpoint and remediate the root cause – Nothing erodes trust in IT as much as an application failure. With AIOps, IT can avoid being surprised by a critical application or infrastructure going down and identify and fix potential issues before they become big problems.
Helps IT leaders optimize their spending on cloud usage and software so they can provide what the business needs when it needs it – Spending money on technology to support the business is a lot like the Goldilocks conundrum: Too little, and the business can’t do what it needs to do to continue to grow and be competitive; too much, and money is being wasted that could be put to better use.
Go from Reactive to Proactive to Predictive Management – Combining the power of AI and Automation allows ITOps teams to provide predictive alerts that let IT teams address potential problems before they lead to slowdowns and outages, and take automated preventive actions to mitigate any potential risks.[Text Wrapping Break]
Achieve faster Mean Time to Resolution (MTTR) – By cutting through IT operations noise and correlating operations data from multiple IT environments, AIOps can identify root causes and propose solutions faster and more accurately than humanly possible. AIOPs drastically improve all key ITOps metrics like MTTR, MTTD (Mean Time To Detect), MTBI, etc.