AIOps: The Key to Reducing Network Failures


Implementing an AIOps solution can help ensure that an IT operations team is equipped to identify and remedy any network inefficiency.

Today’s technology networks are highly complex. Between the rapidly growing cloud environment and millions of edge devices lie myriad layers of heterogeneous network components. The labyrinthine network includes the network cloud engine, software-defined networks (SDNs), broadband network gateway, WDM, private lines, router, switch, apps, and more. Even a sporadic issue in any of these components can result in hours of downtime for all downstream applications – which can cause significant damage to a business. Increasingly, organizations are eyeing AIOps for help.

Why? Network failures are expensive, with downtime costs running into hundreds of thousands of dollars each hour. Moreover, manual troubleshooting is impossible at scale. To manage network operations, reduce failures/outages and preventively remediate network inefficiencies, it is critical that businesses implement an artificial intelligence for IT operations (AIOps) solution efficiently and effectively.

What can AIOps do for network management?

AIOps brings together data analytics, machine learning, and automation to improve IT and network operations efficiency.

A good AIOps engine collects telemetry and workload data from each network and edge device to create baselines. With these baselines, it identifies anomalies, performs root-cause analysis, and recommends optimization. This offers several immediate outcomes for network engineers and IT teams.

Control alert storms

For network engineers, the most valuable outcome of using a robust AIOps engine is controlling alert storms. Over one-fifth of all engineers spend at least a day a week in Wi-Fi troubleshooting. Gartner found that 70% of its clients use a manual command-line interface (CLI) to make their network configurations. In fact, 69% of respondents said that “they had no or little confidence about all the devices on the network.” Traditionally, when there is an outage in one device on a network, the monitoring tool raises hundreds of alerts across all areas impacted by it. This means that IT teams process hundreds of alerts every day, even when the problem is just lie in one or two. AIOps can help intelligently and effectively suppress these alerts — and eliminate false positives — to make sure IT engineers are focussed on genuine issues.

Improve root-cause analysis

From there, a good AIOps engine can help automate the root-cause analysis, accelerating the team’s mean time to respond (MTTR). We’ve seen teams resolving common network-related issues like authentication or connectivity far more quickly than they would without AIOps.

While most AIOps tools perform root-cause analysis post-facto — after the incident has occurred — a great AIOps engine can do it in near real-time, even predicting incidents in many cases. For instance, it can probe anomalies to offer early warning signals for potential problems. It can offer suggestions around impact radius, the landscape of services/instances that are likely to get affected by this incident or similar incidents from the past, etc. Based on this analysis, IT operations teams can take preventive action before the incident occurs. This ability to perform root-cause analysis in real-time and prevent outages can be especially valuable for enterprise security teams.

Prioritize intelligently

Monitoring tools today are very good at understanding patterns and identifying anomalies. Whenever there is an outlier, they raise an alert inviting IT operations teams to analyze whether there is an issue or not manually. Often, network engineers realize that the incident that the monitoring tool identifies as an anomaly is expected behavior.

An intelligent AIOps engine can perform workload-behavior correlation to understand whether an anomaly is a potential issue or merely a natural outcome of the workload. Over time, it can also learn to suggest preventive measures, automating the definition of network thresholds based on projected workloads. As a result, it can increase network utilization, provisioning additional assets only when needed, keeping unnecessary redundancies to the bare minimum.

Ensure compliance

As the epicenter of security attacks from across sources, the network comes under increased compliance scrutiny. This means that all devices on the network need to be regularly monitored to ensure configuration compliance. AIOps can automate this, requiring manual intervention only when standards are breached.

Strengthen security

Organizations often have siloed network and security teams specializing in each area. This often results in root-cause being inaccurately associated, for lack of holistic data. For instance, a security event might impact the performance of a network but be treated as a network issue because the team does not have visibility into security data. AIOps can eliminate problems like this by bringing together data from across the network, enabling more accurate root-cause analysis.

Selecting the right AIOps solution to prevent network failures

The primary ingredient in the success of any artificial intelligence initiative is accurate and high-quality data. When evaluating the tools on the market, here are some key considerations:

Ensure your tool can collect telemetry data, including network-device-generated logs, health metrics, and traffic statistics such as application-specific bandwidth usage, network latency, packet loss, etc. This data should be collected from every part of the technology landscape. By combining data from all the silos, organizations can ensure complete visibility into the connections between the applications, network, and devices.

Collect workload data to correlate it with network performance for more accurate and intelligent anomaly detection. Without this, you will not be able to conduct contextual analysis, restricting the real impact that AIOps can deliver to your network performance.

Choose an AIOps tool that learns continuously. Make sure that it not only can identify anomalies but also evolve through learning from the remediation. At scale, the tool needs to handle most issues, eliminate recurring incidents and strengthen the technology landscape itself.

Explore peer analytics and benchmarking. In a recent survey by EMA, 80% of enterprises said they would like for their AIOps tools to offer peer analytics and benchmarking as a way to stay ahead of the curve. While evaluating an AIOps tool, explore if it can offer such benchmarking without compromising the security and privacy of your data.

Before you decide on your AIOps tool:

  • Set your goals clearly, define your key performance indicators (KPIs) and return on investment (ROI) metrics
  • Carefully consider the regulatory compliance risks and deployment options offered by the AIOps vendor
  • Ensure that it integrates seamlessly with your network and monitoring landscape

In an increasingly digital world, any network disruption such as downtime or slow connections can wreak havoc for a business. Implementing an AIOps solution can help ensure that your IT operations team is equipped to identify and remedy any network inefficiency.


About Ann Hall

Ann Hall is the marketing manager at Heal Software Inc., the innovator of the game-changing preventive healing software for enterprises known as HEAL, which fixes problems before they happen.

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