AIOps for Network Monitoring

by | Dec 22, 2021

Multi-cloud hybrid cloud environments, microservices architectures, the rapid growth in the number of mission-critical applications, and the sudden surge in remote work have made enterprise networks exponentially complex. These networks are often not designed to handle the variety of physical and wireless media that’s become common today, for instance, the number of video calls, data transfer through screen sharing, etc. This often results in brownouts, i.e., unanticipated and unplanned drops in network quality.

A study by Netrounds found that the average loss of network brownouts goes up to $700,000. With application performance itself being heavily reliant on network performance, and the concerns of security mounting, enterprises with suboptimal systems are at significant risk.

Yet, most network issues are still undetected by monitoring systems. An issue is even brought to the attention of the enterprise only when users flag it, significantly affecting customer experience. When an incident is raised by a user, network managers manually perform root-cause analysis to identify the problem and enable fixes.

This happens primarily for the following reasons:

Lack of complete network visibility: Network managers have no single-pane view of their entire network, making it difficult to see problems even as they occur, let alone in advance. Intermittent issues are even more difficult to track.

Fragmented tooling: Enterprises often use myriad monitoring tools that collect a wide range of data. But this data doesn’t translate to insights.

Manual analysis: Given most monitoring tools perform only that — monitoring — much of the analysis and remediation continues to be manual. This takes up too much of the network operations engineer’s time, making the overall process reactive and ineffective.

To overcome these challenges and ensure network strength, enterprises need to look beyond their current network monitoring systems. They need to leverage advanced artificial intelligence technologies to predict incidents before they occur and prevent them through autonomous remediation. In short, enterprise network operations teams need AIOps.

AIOps for Network Monitoring

For the uninitiated, AIOps is the practice of leveraging artificial intelligence and machine learning in IT operations. At the basic level, AIOps can dynamically perform root cause analysis, conditional automation using correlation and execute pre-defined workflows. The most significant value of AIOps, however, is in self-healing and autonomous remediation. Here are some of the most common network monitoring use cases that AIOps can help with.

Intelligent root-cause analysis: Application failures can have a wide range of causes. Let’s say an application has failed as a result of a code error. This code sits on a database, which is on a server. A good AIOps engine can process data from across the application, databases, servers, and other network components to automatically identify the root cause as the code error. This way, the operations teams can get to the bottom of the issue without being blinded by organizational silos.

Dynamic baselining: As the scale and complexity of the enterprise network grows, you need to adjust the baselines at regular intervals to maintain performance levels. A good AIOps solution can do this dynamically by collecting data from various sources and correlating them intelligently.

Autonomous provisioning: Workloads are hardly static. Based on the time of the day, the season of the year, usage, necessity, etc., workloads demand varying levels of resources. For instance, an e-commerce app would need higher memory and compute on Black Friday. A robust AIOps tool will make workload-behavior correlation, predict bottlenecks, and provision resources appropriately.

Configuration management: Enterprises regularly install new devices or repair existing devices on the network. Each time, it needs to be appropriately configured and tested thoroughly to ensure seamless performance. A good AIOps tool can ensure that this occurs every single time without any manual effort.

Compliance monitoring: Whether regulatory or internal, compliance is fundamental to network maintenance. A good AIOps tool can conduct advanced network troubleshooting like routine protocol monitoring and alerting if unscheduled changes appear. A great AIOps tool can also take automated actions to resolve these issues.

Planning network growth: Unlike traditional IT operations, which is focused on post-facto resolution of incidents, AIOps is preventative, enabling organizations to build resilience for the future. With a good AIOps platform, you can perform what-if analysis on traffic patterns to predict performance to meet projected traffic volumes. In virtualized networks, you can also be alerted to over-provisioning bandwidth (leading to unnecessary capital investment) or under-provisioning (leading to a potential bottleneck), so you can take corrective action.

An essential part of application performance monitoring (APM) is network maintenance. Bringing your network monitoring into the AIOps gambit opens up significant opportunities for cost savings, improving efficiency, boosting employee productivity, and protecting your bottom line.