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Understanding How AIOps Work for Modern Cloud Architecture

Understanding How AIops Work for Modern Cloud Architecture

A majority of IT professionals use AIOps to define the role of Artificial intelligence in IT operations. However, AIOps actually stands for “Algorithmic IT Operations.” In recent years, largely thanks to the massive advancements in the field of AI and machine learning capabilities, AIOps has become critical for monitoring and controlling hybrid, dynamic, distributed, and componentized IT systems. From building CI/CD structures to leveraging modernized predictive analytics for remote anomalies detection, there is a huge scope for IT professionals working on AIOps projects.

Why do we need AIOps?

Traditional IT practices are heavily outdated in the current context of digital transformation goals that seek agility, accuracy, and real-time reporting in every aspect of IT operations management. Way back in 2017, Gartner had predicted that AI and Machine Learning (AI ML) would become key functionalities to support IT systems. Since then, IT companies are seeking innovative ways to include and leverage AI, ML, and analytics for various key IT operations, such as event correlation, anomaly detection, and causality determination.

Now, from where we are perched today, we can clearly understand AIOps enables IT Ops and DevOps teams to work smarter and faster by analyzing IT data algorithmically, allowing them to detect and handle digital-service issues earlier, before business operations and consumers are harmed.

Let’s dive deeper into the world of AIOps. 

What is AIOps?

AIOps stands for artificial intelligence in IT operations. AIOps solutions, which collect data from a variety of IT operations tools and devices in order to detect and respond to issues in real-time while also providing typical historical analytics, employ big data.

In a general ecosystem for IT operations management, most architects prefer to use AIOps as a multi-layered framework that simplifies the way AI, Machine learning, Big Data analytics, and Predictive/ Prescriptive intelligence converge at a point. The two main components of AIOps are big data and machine learning. It is vital to shift away from segregated IT data in order to aggregate observational data (such as that found in monitoring systems and job logs)

AIOps then uses the combined IT data to develop comprehensive analytics and machine learning strategy. Automation-driven insights that lead to ongoing improvements and repairs are the desired outcome. Continuous integration and deployment (CI/CD) for key IT functions might be thought of as AIOps.

AIOps brings together three key IT disciplines to achieve the goal of continual insights and improvements:

  • Performance management
  • Performance management
  • Real-time Action

AIOps develops a strategy that recognizes that, in our increasingly fast IT environments, a new approach based on big data and machine learning is required.

The elements of AIOps

1. Extensive and diverse IT data.

AIOps is based on bringing together varied data from both IT operations management (ITOM) (metrics, events, etc.) and IT service management (ITSM), as indicated by the black and blue chevrons (incidents, changes, etc.). Bringing data from various technologies together so they can “talk” to one other and speed root cause discovery or enable automation is referred to as “breaking down data silos.”

2. Aggregated big data platform.

To support next-level analytics, data must be brought together when it is released from siloed platforms. This must happen not only in the background, as in a forensic investigation using previously collected data, but also in real-time as data is absorbed. My other post has further information on AIOps and huge data.

3. Machine learning.

Machine learning may be used to analyze enormous amounts of diverse data using big data. This isn’t possible without first combining the data, and it’s also not possible with manual human effort. Manual analyses are automated, and new analytics on new data may be performed at a scale and pace that would be hard to do without AIOps.

4. Observe. 

This is the evolution of the traditional ITOM domain, which now includes development as well as non-ITOM data (topology, business indicators) to enable additional correlation and contextualization modalities. When combined with real-time processing, probable-cause detection and issue generation become one and the same.

5. Engage.

 Bi-directional communication with ITOM data to enable the above analyses and auto-create documentation for audit and compliance/regulatory requirements is part of the traditional ITSM domain’s evolution. Cognitive classification, routing, and intelligence at the user touchpoint, such as chatbots, are examples of how AI/ML expresses itself here.

6. Act. 

This is the AIOps value chain’s “final mile.” If responsibility for action is returned to human hands, automating analysis, workflow, and documentation will be for naught.

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How AIops Work for Modern Cloud Architecture

To bring diverse IT operations data together, AIOps uses a big data platform. This data contains the following categories of information:

  • Streaming real-time operations events
  • Historical performance and event data
  • System logs and metrics
  • Network data, including packet data
  • Incident data and ticketing
  • System logs and metrics

AIOps then uses ML and focused analytics to achieve the following results:

  • Separate significant event alerts from the ‘noise’:

AIOps comb through your IT operations data using analytics like rule application and pattern matching to identify signals—significant abnormal event alerts—from noise.

  • Identify root causes and propose solutions: 

AIOps may utilize industry-specific or environment-specific algorithms to correlate anomalous events with other event data across environments in order to diagnose the source of an outage or performance problem and offer solutions.

  • Learn continually, to improve the handling of future problems:

Based on analytics findings, machine learning skills can update or construct new algorithms, allowing for earlier detection of problems and more effective solutions.

AIOps benefits

The main advantage of AIOps is that it allows IT operations to discover, address, and resolve slowdowns and outages faster than they could manually sort through alerts from multiple IT operations systems. As a result, there are a number of distinct benefits:

  • Achieve faster mean time to resolution (MTTR): 

AIOps can detect fundamental problems and provide solutions faster and more accurately than humans can because it cuts through IT operations noise and correlates data from diverse IT environments. This enables firms to set and accomplish MTTR targets that were previously unthinkable. Nextel Brazil, for example, was able to reduce issue response times from 30 minutes to less than 5 minutes by implementing AIOps.

  • Go from reactive to proactive to predictive management:

 AIOps keeps becoming better at spotting less-urgent warnings or signals that connect with more-urgent situations because it never stops learning. As a result, it may send out predictive alerts, allowing IT staff to resolve possible issues before they cause slowdowns or disruptions.

  • Modernize your IT operations and your IT operations team:

Instead of receiving every alert from every environment, AIOps operations teams only receive alerts that meet certain service level criteria or characteristics, replete with all the context needed to make the best diagnosis and take the best and fastest corrective action. The more AIOps learns and automates, the more it can assist in keeping the lights on with minimal human work, allowing your IT operations team to focus on duties that are more strategic to the company.

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4 Ways to Improve CloudOps Efficiency with AIOps

1. Managing Enterprise Cloud Costs

Cloud cost issues are presenting big concerns for finance, product, and engineering departments within organizations due to dynamic provisioning, auto-scaling support, and a lack of underused cloud resources garbage collection. When hundreds of engineers inside a firm use Cloud platforms like AWS, AZURE, and Google for their projects, it will be hard for one person to keep track of spending or establish any centralized authorization mechanisms.

Machine intelligence and artificial intelligence (AI) technologies are being used by several companies, including Botmetric, to detect expense spikes, provide comprehensive visibility into who used what, and assist businesses in applying intelligent automation to remove unused resources.

2. Ensuring Cloud Security Compliance

How can businesses ensure that every cloud resource is provisioned with the right security compliance configuration for their business and meets regulatory requirements such as PCI-DSS, ISO 27001, HIPPAA, and others when any engineer within the organization can provide a cloud resource via API call? This, again, necessitates real-time security compliance detection, informing the appropriate person who provisioned the resource and taking steps such as shutting down machines if compliance is not met.

Continuous monitoring is the most critical aspect of security these days, and this can be accomplished if you have a mechanism in place that detects and reports the next millisecond when an alarm is received. Many organizations are working on solutions that not only detect but also automatically resolve vulnerabilities. Companies may stay compliant and reduce their business risk by adopting AIOps and employing real-time event configuration management data from cloud providers.

3. Reduce Alert Fatigue

Too many alerts are a well-known concern in the data center business, and it’s commonly referred to as Ops fatigue. In the cloud world, the conventional NOC team (reviewing alert emails), IT support team (reviewing tickets and responding), and engineers looking into major problems were all broken, with DevOps Engineers managing all of these jobs.

Also, anybody who has managed production infrastructure, business services, applications, or systems architecture knows that the majority of problems are caused by predictable events or patterns. The universal denominator in any IT operations management is noisy alerts. With a swarm of notifications entering inboxes, it’s tough to keep track of which ones are important or should be examined by engineers. Filtering out unneeded alerts or suppressing duplicate notifications for more concise alert management to detect serious issues and foresee problems is a wonderful solution driven by anomaly detection.

When particular events or symptoms occur in their application or production infrastructure, the engineers already know what to do. When events or alerts are generated, most current tools just provide a textual description of what occurred rather than providing context for what is occurring or why it is occurring? So, as DevOps engineers, it’s critical that you write diagnostic scripts or programmers to figure out why your CPU rose. What caused an application to go down? Alternatively, why has API latency increased? To put it another way, intelligence is being used to help us get to the base of the problem faster.

4. Intelligent Automation for Operations

Engineers in charge of managing production operations (from ITOps to DevOps) have been frustrated by static tooling that is generally ineffective. We will see more dynamic tooling that can support them in day-to-day operations as machine intelligence and deep learning become more prevalent. The only magic wand for tackling operational challenges in the Cloud is the use of code and automation as a weapon.

It would simply add to the complexity of your DevOps teams if you didn’t use clever automation to operate your cloud infrastructure. Everything from automatic cleanup activities to alarm diagnostics can be created. As a team and as a DevOps engineer, you must concentrate on leveraging CODE to solve problems. If you’re just starting to develop your CI/CD pipeline, you should definitely include a trigger that can monitor deployment for health metrics and prompt a rollback if it finds performance or SLA issues. Simple solutions like these can save hours of work after each deployment and gracefully handle errors.

Conclusion

The application of tried-and-true technologies and methods to ITOps is known as AIOps. ITOps employees are typically slow to adopt new technology since our professions have always necessitated conservatism. ITOps is responsible for keeping the lights turned on and the infrastructure that enables organizational applications to run smoothly.

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[To share your insights with us, please write to sghosh@martechseries.com]

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