AiThority Primers Update: An Introductory Guide to AIOps

Every year the tech industry is inundated with countless trends of what will be the “next big thing.” Terms that many have been boasting as the next step in our technological evolution like Big Data, Machine Learning, and of course, AIOps may have been easy to ignore for a time, but not anymore. As a matter of fact, industry experts have predicted that AIOps will be the “next big thing” for Information Technology Operations (ITOps), believing that artificial intelligence (AI) and machine learning (ML) would transform ITOps procedures and reconstruct IT ecosystems. They were right. Over the years, AIOps has been gaining interest at an exponential rate, especially since the traditional processes are no longer applicable due to digital transformation.

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So, What Does All of That Mean for You?

First, we need to quickly define AIOps so we’re all on the same page. Simply put, AIOps is artificial intelligence for IT operations. Gartner coined the term and defines it as such, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” It bridges three different IT disciplines: service management, performance management, and automation.

How Does It Actually Work?

AIOps platforms typically do three things:

(1) Ingest data from multiple sources, both real-time and historical;

(2) process, correlate, contextualize this big-data via statistical and probabilistic methods to identify, trends and patterns, anomalies, thereby leading to root cause determination and thus predictive insights; and

(3) recommend or enact remedial measures, preferably via automation.

As modern-day networks become increasingly complex strongly driven by digital transformation needs, AIOps techniques have been recently applied in the networking space both by network infrastructure vendors as well as network management vendors to manage operational scale.

End Users have thus been able to leverage these advanced analytics, where available, and utilize that learning to automate operations that are handled by a business’ ITOps team. This creates a snapshot of what is a “normal” state of function for the network, thus providing insight into any abnormalities that are affecting the network. These insights might include, for example, an impending saturation of SDWAN capacity in a few sites (out of hundreds) due to an increase in usage of certain chatty applications.

Or, anomalous traffic behavior that predicts an impending attack. These insights enable ITOps teams to move at a much faster pace with increased agility to solve impending issues in the network.

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How Can IT Teams Benefit From Using AiOps?

By providing intelligent, actionable insights that drive a higher level of understanding of normal and not-normal states of the network, an ITOps team can continuously improve, focusing an organization’s priorities on the root causes of issues.

The overarching benefit is that ITOps teams are able to identify, address, and resolve slow-downs and outages faster than they could by sifting manually through alerts from multiple IT operations tools. A classic scenario is in the SDWAN environment where SDWAN tunnels are established over WAN links and carry application traffic that is routed based on SLA requirements. When applications in this scenario have issues, it’s good to correlate application performance with tunnel performance, and in turn with WAN uplinks performance.

Also, it’s important to understand if all applications traversing the same setup have the same performance degradation.

What would a possible solution for this be?

Would modifying SDWAN policies dynamically – or mapping applications to QoS policies – make an impact?

More importantly, can we predict the impact based on a few parameters that are starting to trend? This type of AIOps driven triaging results in many added benefits including reduced MTTR, modernizing of IT departments and teams, and being able to shift to predictive management as opposed to reactive.

What Are Some AIOps Use Cases?

AIOps is incredibly valuable to optimizing IT operations and processes, giving visibility and support to IT teams that were previously done through several tools and solutions. For example:

• Optimizing Day 2 Network Operations: Most large enterprise network organizations have tool sprawl. Each monitoring tool looks at specific data sources and thus specific issues. For instance, NPMD (network performance monitoring and diagnostic) tools look at the network performance, APM (application performance monitoring) tools look at application performance, ITIM (IT infrastructure management) tools look at network infrastructure issues. AIOps platform can look into these disparate data sources and correlate data points thereby identifying patterns that may not be seen otherwise.
• Cloud Adoptions/Migrations: Many organizations have already begun transitioning to the Cloud or have begun transitioning to a hybrid Cloud solution. By providing clear network visibility, AIOps can dramatically reduce the operational risks of cloud migration and a hybrid Cloud approach. Key capabilities that AIOps brings to the table include understanding pre-migration traffic baselines and corresponding SLAs, correlating network KPIs to corresponding quality of experience, generating baselines post-migration, and establishing a new normal state for both the applications and the hybrid network.
• Digital Transformations: The right AIOps solution should support an enterprise’s agility and allow ITOps teams more flexibility. This ultimately gives the organization more freedom to pursue and plan different strategic business implementations, without putting unnecessary burden onto the IT teams. For instance, when rolling out a new global application, understanding existing baselines, trends, and thresholds are important so that additional application traffic will not have unintended consequences.
• Another example is rolling out a new network paradigm such as SDWAN that has a whole lot of intended and unintended consequences that have to be addressed in a data-driven fashion.

What’s Next for AIOps? 

AIOPs may seem like a daunting task or a tool that may be something worth investigating down the road. But consider how ML and AI has changed many industries thus far. AIOps is a valuable tool for ITOps now, and when utilized properly, can make their jobs easier, while reducing costs and MTTR. As modern-day networks increase in complexity and in scale, and with requirements to support an unending line of new applications, traditional solutions and approaches will increasingly fall short.

No team wants to be saddled with unwieldy, antiquated technology or tools.

If your team is at the beginning of your AIOPs journey, consider these three tips to get you started.

(1) Start small by identifying and focusing on a single use-case, such as event correlation to reduce MTTR for network and application performance.

(2) Align AIOps initiatives with the 3-5 year technology roadmap so there is bidirectional alignment with requirements and benefits that can be integrated with the ecosystem platforms.

(3) Build strong ties with Business initiatives so AIOps platforms can deliver value from the network side that helps monetize the network.