Data Analytics

AIOps Observability Help Admins Gain an Edge Over Network Disruptions

Newer options let IT managers proactively assess a network’s state through data analysis and activity monitoring.

Krishna Sai is senior vice president of engineering at SolarWinds.

Agencies are going bigger and getting bolder with their use of hybrid and multicloud environments, even as they attempt to simplify their cloud infrastructures.

The Department of Defense recently announced that it would work with Amazon, Google, Microsoft and Oracle to form its Joint Warfighting Cloud Capability network. Meanwhile, public sector cloud spending has grown to more than $8 billion, according to Deloitte.

But the more agencies invest in the cloud and adjacent technologies such as edge devices and 5G, the more complicated things get. Agencies rely on these solutions in combination with artificial intelligence (AI) and machine learning to provide actionable, predictive intelligence derived from many disparate data sets.

The problem is that the data sets and the tools required to process them have become so complex that it’s nearly impossible to uncover the root cause when something goes wrong, at least through traditional network monitoring. An error anywhere in the system can take hours or even days to discover. This can disrupt an agency’s ability to gain insights, making it difficult to complete its mission. All the while, IT managers will undoubtedly be dealing with a flood of alerts capable of overloading their senses and leading them down the wrong path.

Fortunately, there are better options for this type of complex environment: observability and AI for IT operations, or AIOps.

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Observability Offers a Complete View

Traditional network monitoring takes a reactive approach to network management. Observability allows IT managers to proactively assess a network’s state through data analysis and network activity monitoring. With an observability system, managers can see everything going on within their networks, whether they are on-premises, hybrid or multicloud.

Observability is defined by four key components, known as MELT:

Metrics help identify what’s wrong with the components of the network.
Events help prioritize important alerts and minimize the excess noise IT managers must filter through before discovering mission-critical issues.
Logs identify the reasons an issue is occurring.
Traces lead managers directly to problems, eliminating the need for guesswork.

Through observability, IT managers can discover and map dependencies through their infrastructures, networks and applications. This helps them understand how an anomaly in one might impact the others.

Complete visibility allows them to quickly respond to the problem without spending hours hunting it down. Automation takes things further, cutting out the need for human intervention.

AIOps Is Powerful and Proactive

Observability is about more than just ensuring the network is running smoothly. When paired with AIOps, it becomes a powerful tool for ensuring network resiliency and uptime.

The concept of AIOps was introduced by Gartner in 2016. Although it’s been around for a while now, many organizations are only recently leveraging the technology as AI and machine learning mature. Those who aren’t should consider doing so because AIOps helps administrators rapidly respond to issues wherever they exist within their networks.

AIOps combines AI, machine learning and natural language processing to collect disparate information and large data sets to identify issues, report them to IT and offer intelligent recommendations. It allows IT teams to quickly pinpoint issues, whether they emanate from private or public clouds or a combination of the two.

AIOps culls information from the MELT components — including historical data derived from past incidents — and uses it to automatically remediate errors. In cases requiring a human touch, it can provide IT administrators with actionable recommendations on how to rectify a problem.

Without proactive observability and the intelligence provided by AIOps, administrators may never know problems exist within their cloud environments. The disruption may continue unabated, slowly deteriorating the network or limiting its effectiveness in providing access to resources, including applications and data. Administrators may also become overwhelmed by a chorus of alerts indicating an issue but failing to point them to a location.

With observability and AIOps, the noise is turned down, the problems are quickly identified and administrators can respond to issues before they disrupt network operations. Agencies can continue to collect their data, run their clouds and access their applications, and they don’t have to worry about compromising their ability to receive actionable intelligence.

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