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How AIOps enables next-generation networking
As networks grow more complex, management becomes more difficult. AIOps can help network administrators transition to and manage next-generation networks.
Network environments require new levels of automation and intelligence that far outpace manual IT capabilities. The volume and velocity of operational data outstrip the network team's ability to provide efficient oversight and maintenance.
However, that wealth of data, combined with analytics and machine learning (ML), is critical for enabling AIOps, a cutting-edge approach for organizations to ensure high-performance IT services. AI networking, a subset of AIOps, ensures real-time network monitoring and proactive remediation, while reducing complexity and enhancing overall network performance.
This article examines the emergence of AIOps as a tool to enable a transition to next-generation networking.
AIOps necessary for modern networking
Network infrastructures are shifting from monolithic foundations and legacy networks to distributed architectures with advanced technologies. Microservices -- loosely coupled and independent components -- provide the low latencies and fast scalability that modern applications and digital services require.
However, because these innovative technologies have made management more complex, it's more complicated to maintain consistent performance in these next-generation networks. The following are examples of technologies that complicate network management:
- Virtual networks.
- Software-defined WAN (SD-WAN).
- Cloud.
- Edge environments, such as IoT, 5G and smart grid.
This is where AIOps comes in. AIOps is a platform that processes massive amounts of unstructured information with ML algorithms. AIOps capabilities enable IT teams to accomplish the following:
- Identify system patterns, anomalies and trends.
- Analyze traffic patterns.
- Predict problems.
- Implement deep learning workflows to respond to network issues through faster remediations.
- Detect, isolate and mitigate cyberthreats.
AI networking focuses on automating the networking aspect of infrastructure. It offers a focused extension of these capabilities to improve network functionality, while reducing the IT burden. Moreover, as a core component of AIOps, AI networking enables fast scalability, reduces complexity and promotes network agility.
Putting AIOps into action
AIOps is an approach that enhances IT operations and supports business strategies. Instead of manual IT responses, which are reactive, administrators can use AIOps to monitor applications and services automatically across multiple infrastructures, including corporate, public and third-party networks.
It's difficult to understate the advantages of proactive remediations gained through AIOps. Network vulnerabilities that lead to incursions continue to rise each year, which increases the need for cybersecurity urgency. By analyzing historical data and using ML, AIOps can help network teams identify suspicious cyberactivity and use predictive insights to reduce network latencies and ensure rapid recoveries.
However, data quality issues present significant challenges to an effective AIOps platform. AIOps requires adequate data to analyze, predict and remediate issues, and data quality issues can affect the reliability of AIOps by providing inaccurate analyses or misleading conclusions. These issues are especially apparent during data mining, a process by which tools extract insights and knowledge from a system.
Organizations with AIOps platforms collect and analyze different types of telemetry data for mining purposes, including the following:
- Network flows.
- Configuration files.
- Core services data.
Once teams properly analyze this data, they can improve network monitoring and observability to ensure all on-premises and external networks run efficiently.
For example, AIOps applies ML algorithms to all collected data to distinguish between minor and major events, filtering out irrelevant ones. This reduces the volume of unnecessary alerts IT teams frequently receive, which helps them focus on important issues. AIOps enables administrators to analyze network performance and identify the next steps to troubleshoot and remediate as needed.
AIOps for next-generation networking
Once network teams have the capabilities to manage complex enterprise networks through AIOps, they can modernize their infrastructures and implement new technologies.
These technologies are those that typically constitute next-generation networks, including the following:
- SD-WAN and secure access service edge. SD-WAN and SASE connect distributed users to a corporate network and provide them with access to its resources. This is especially critical as remote and hybrid work become commonplace in enterprise networks. AIOps is essential to enable network administrators to manage vast amounts of data, monitor security and troubleshoot network issues within these platforms.
- Next-generation Wi-Fi standards. As newer versions of Wi-Fi, such as Wi-Fi 7, gain traction in enterprises, organizations can benefit from using AIOps to support the advanced and complex features the new connectivity options provide.
- Private 5G. Private 5G networks are owned, operated and managed by a business network. Organizations interested in private 5G require enhanced capabilities over traditional public 5G networks to have greater control and provide the secure connectivity of a private 5G infrastructure. AIOps, through its ability to proactively manage a network, enables organizations to implement private 5G with reduced complexity.
Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.
