Paul Stuttard, Director, Duxbury Networking.
According to a recent article in the Financial Times, “The Future of Networking Beyond 2025,” corporate networks will look very different in 2025 and beyond, driven by constant innovation and massive increases in data usage.
The editorial emphasized that “business and technology executives are already thinking differently about networks as new operating models and hybrid working become more entrenched.” Along with these developments, there have been significant advances both in network infrastructure and technology and in the way networks are managed. ”
As organizations embrace online collaboration and remote working as the norm, the workforce will undoubtedly become more hybrid, with more employees working from home or splitting their time between home and the office.
From an enterprise network perspective, this means the need to support the thousands of unique network locations and connections that characterize this hybrid work environment, while enabling the network to handle significant increases in voice and video traffic. It means that there is.
According to the Financial Times, AIOps (artificial intelligence for IT operations) is becoming essential for networks to achieve these objectives while delivering a more seamless and personalized user experience.
The main benefit of AIOps working with intelligent networking is the ability to establish a more proactive and dynamic networking architecture.
AIOps is a term coined by Gartner in 2017. Refers to the practical application of artificial intelligence (AI) to enhance, support, and automate IT operations. This refers to a platform that leverages machine learning (ML) and analytics to automate decision-making and enhance observability.
This represents a major shift in the concept of enterprise information technology (IT) as we previously understood it, and AIOps is poised to become a key part of next-generation IT.
Taking next-generation IT models a step further, when AIOps is integrated with intelligent networking concepts, it opens the door for organizations to achieve new levels of efficiency, agility, and resiliency in their network operations.
The key benefit of working with AIOps and intelligent networking is that it provides organizations with the ability to establish a more proactive and dynamic networking architecture, giving them an edge in an ever-evolving and increasingly competitive digital world.
“Big data” analytics company Clairvoyant's CTO, Shekhar Vemuri, says:runtime system [employed by corporations]. Understanding the interactions between different systems and their impact on costs, SLAs, outages, etc. can be very difficult. ”
Vemuri points out how the combination of intelligent networking and AIOps streamlines network management processes. He says it can quickly and accurately automate routine tasks such as network monitoring, troubleshooting, and optimization, allowing IT teams to focus on strategic initiatives rather than routine maintenance.
Some industry analysts go further, saying that the integration of intelligent networking and AIOps is a “new networking paradigm” that can lay the foundation for tomorrow's “self-driving, self-healing networks” with near-zero downtime. It suggests that all problems can be proactively detected and resolved. ”.
In these networks, AIOps leverages ML algorithms to analyze vast amounts of network data in real-time to achieve the goal of “self-driving, self-healing.”
Practical benefits of integrating intelligent networking and AIOps include enhanced monitoring of network performance as AI algorithms can detect anomalies and deviations from normal network behavior. This enhances proactive troubleshooting and supports faster response to potential issues.
This predictive capability transforms network management from reactive to proactive, improving network reliability and uptime while minimizing downtime and maximizing productivity.
Another benefit is the automation of incident resolution. Intelligent networking using an AIOps platform can identify patterns to predict potential incidents before they occur and automate the incident resolution process, significantly reducing the average time to resolve network issues. This leads to cost reduction and improved operational efficiency.
Additionally, this integration facilitates dynamic resource allocation based on real-time demand, which is often determined by fluctuating workload patterns. This allows efficient use of bandwidth while minimizing congestion. The result is increased consumer satisfaction, improved network performance, and minimized operational costs.
In today's digital environment, networks are becoming more dynamic and complex due to the demand for edge computing, cloud services, and Internet of Things devices.
Against this backdrop, intelligent networking and AIOps work together to deliver benefits such as predictive capacity planning through analysis of historical data, current usage patterns, and future growth projections.
This is a model that allows edge networks to independently implement automation, programmability, predictive analytics, and orchestration. This gives network operators the agility and flexibility they need to not only respond to changing business requirements, but also maintain a strong security posture.
From a security perspective, the integration of intelligent networking and AIOps will enable the development of adaptive network security measures, allowing future networks to autonomously modify previously approved security policies and configurations to create new This will allow you to reduce the threat.
This adaptive approach increases resilience to evolving cyber threats and enables networks to dynamically defend against increasingly sophisticated attacks in the future.