Revolutionising the network

When AIOps is integrated with intelligent networking, organisations can unlock new levels of efficiency, agility and resilience in their network operations.
Paul Stuttard
By Paul Stuttard, Director, Duxbury Networking.
Johannesburg, 08 Apr 2024
Paul Stuttard, director, Duxbury Networking.
Paul Stuttard, director, Duxbury Networking.

According to a recent article in the Financial Times, entitled “The future of networking in 2025 and beyond”, constant innovation and a massive increase in data usage will see corporate networks looking very different from 2025 onwards.

The editorial emphasised how “business and technology executives are already thinking about the network differently as new operating models and hybrid working become more entrenched. Alongside these developments are significant advancements in both network infrastructure and technology and the way networks are managed.”

There is no doubt that as organisations accept online collaboration and remote working as the norm, the workforce will become increasingly hybrid, with more employees working from home or dividing their time between home and office.

From the corporate network’s perspective, this means having to support thousands of unique network locations and connections that will characterise this hybrid work environment, while ensuring the network can manage a significant increase in voice and video traffic.

According to the Financial Times, AIOps (artificial intelligence for IT operations) is becoming essential in order for networks to meet these objectives, while enabling more seamless and personalised user experiences.

The key advantage of AIOps’ collaboration with intelligent networking is the establishment of a more proactive and dynamic networking architecture.

AIOps is a term coined by Gartner in 2017. It refers to the practical application of artificial intelligence (AI) to enhance, support and automate IT operations. It alludes to platforms that leverage machine learning (ML) and analytics to automate decision-making and enhance observability.

This represents a significant shift in the previously-recognised concept of enterprise information technology (IT), setting the stage for AIOps to become a critical part of next-generation IT.

Taking the next-generation IT model a step further, when AIOps is integrated with the concept of intelligent networking, the door is opened for organisations to unlock new levels of efficiency, agility and resilience in their network operations.

The key advantage of AIOps’ collaboration with intelligent networking is the establishment of a more proactive and dynamic networking architecture, giving organisations the capability to stay ahead in an ever-evolving and increasingly competitive digital world.

Shekhar Vemuri, CTO of “big data” analytics company Clairvoyant, says: “In this rich, heterogeneous, distributed complex world, it can be a challenge to stay on top of vast amounts of machine-generated data from all the monitoring, alerting and runtime systems [employed by corporations]. It can get extremely difficult to understand the interactions between various systems and the impact they are having on cost, SLAs, outages, etc.”

Vemuri points to how intelligent networking coupled with AIOps promises to streamline network management processes. He says the automation of mundane tasks, such as network monitoring, troubleshooting and optimisation, can be performed swiftly and accurately, allowing IT teams to focus on strategic initiatives rather than routine maintenance.

Some industry analysts go further, suggesting the integration of intelligent networking and AIOps is “a new networking paradigm” capable of laying the foundation for tomorrow’s “self-driving, self-healing networks that will proactively detect and resolve all issues with near-zero downtime”.

In these networks the “self-driving, self-healing” goal will be achieved as AIOps leverages ML algorithms to analyse vast amounts of network data in real-time.

Practical benefits of the integration of intelligent networking with AIOps include the enablement of enhanced network performance monitoring as AI algorithms can detect anomalies or deviations from normal network behaviour. This enhances proactive troubleshooting and supports faster responses to potential issues.

This predictive capability transforms network management from a reactive into a proactive discipline and leads to improved network reliability and uptime, while minimising downtime and maximising productivity.

Another benefit is automated incident resolution. As intelligent networking with AIOps platforms can identify patterns and predict potential incidents before they occur or automate incident resolution processes, mean time to resolution for network issues can be significantly reduced, leading to cost savings and operating efficiencies.

Further, the integration facilitates dynamic resource allocation based on real-time demand often dictated by fluctuating workload patterns. This ensures efficient use of bandwidth with minimum congestion. As a result, consumer satisfaction levels increase, network performance improves and operating expenses are minimised.

In today's digital environment, networks are becoming more dynamic and complicated due to the demands of edge computing, cloud services and internet of things devices.

Against this backdrop, advantages such as predictive capacity planning through the analysis of historical data, current usage patterns and future growth projections can be realised when intelligent networking and AIOps join forces.

It’s a model that permits edge networks to independently implement automation, programmability, predictive analytics and orchestration. This provides network operators with the agility and flexibility needed to not only meet changing business requirements but also maintain a strong security stance.

From a security standpoint, the integration of intelligent networking with AIOps allows for the development of adaptive network security measures, allowing future networks to autonomously revise previously-approved security policies and configurations to mitigate emerging threats.

This adaptive approach enhances resilience against evolving cyber threats and ensures networks can dynamically defend against the increasingly sophisticated attacks of the future.