AI’s Impact on Networking
Gone are the days when enterprise networking was all about connecting machines with cables and crawling under dusty desks to chase wires. At present, setting up a fully functioning network can be done with just a few clicks or lines of code. However, with ever-increasing demands, it’s important to find new solutions to ensure that network infrastructure remains reliable, secure, and agile.
AI is the perfect tool to help with this.
AI is a popular conversation topic with plenty of media coverage over the last few years; we are now starting to see AI innovate and change the way things are done in a wide range of industries. Many organizations are leveraging this valuable and versatile technology to drive innovation.
But how is it benefiting the networking world?
By utilising AI and ML, businesses are offloading low-level tasks, learning more about how their networks are being used, whilst analysing and optimising the performance and the security of their infrastructure.
Cyber Security
AI can help companies detect cyberattacks faster. AI and ML can help a computer find anomalies and predict threats more accurately than the average human. Typical technology relies on stale data that cannot provide new scenarios and methods of data protection the way AI can. AI can process large volumes of cyber threats on networks to fill in the gaps and leave more options for protecting data.
By analysing network patterns and abnormalities, AI can raise the alarm if it detects an anomaly within the network — allowing network admins to identify issues before they become critical problems. This, paired with automatic troubleshooting and self-healing capabilities, enables engineers with AI-integrated infrastructure to be confident that their network will remain stable and operational around the clock.
Network security is a critical area that AI can help within a network. Thanks to ML, AI security solutions can learn from previous threats and attacks to create a resilient defence against future security threats and anomalies.
Meet AWS GuardDuty.
AWS GuardDuty is a great example of a networking security tool that uses ML to train and identify potentially malicious traffic. For more on AWS GuardDuty find out here.
Network Reliability and Enhanced Network Management
Network components may generate low-priority alerts when issues such as congestion, intermittent faults, or re-routing occur. These alerts may go unnoticed by the network monitoring team as they do not directly affect the service. However, these alerts could be early warning signs of underlying problems. By utilising appropriate tools, these weak signals can be amplified to shed light on the underlying issue.
The vast amount of real-time data from the network allows operators to quickly separate the signal from the noise using ML and AI capabilities to distinguish hidden patterns that would be otherwise missed. Utilising AI and ML capabilities enables a cutting-edge approach to an otherwise reactive task, allowing work to be scheduled to quickly fix the problem before it becomes an issue for customers.
AI-based Software-Defined Networking (SDN)
Software-defined networking (SDN) adopts the concept of programmable networks by using logically centralised management allowing us to decouple the data plane from the control plane, facilitating network configuration, management and automation by taking away the management complexity of our internal systems.
The use of AI within SDN has had a significant impact, with various studies showing a huge increase in network and performance efficiency gains. One study has shown an increase in the Quality of Experience (QoE) by 24.1% and a decrease in frame loss rate by 89% and 70% respectively.
Key market leaders in providing AI-based SDN services include the likes of Cisco and Dell, networking technology long-standers with an ever-growing reputation for providing the best services to their customers. AI is no expectation with the market growth in AI-based SD-WAN solutions increasing year-on-year.
What does AI hold for the future of networking?
Throughout my time within the complex world of networking and infrastructure, there has been a significant change in the landscape. The combination of AI and network automation has the potential to solve the toughest challenges in networking. As networks develop and become inevitably more complex as more devices are being connected to networks, the probability of things going wrong also increases.
To manage this and an ever-growing quantity of IoT devices, AI will play a significant role in automating and optimising any hands-on tasks that may arise.
However, the most significant advantage of AI in networking is its ability to understand and analyse network patterns and abnormalities and detect cybersecurity events faster than ever.
Consequently, engineers can concentrate on more significant goals that have a strategic impact rather than the day-to-day mundane tasks that were otherwise the norm.
About the Author:
Laura Gardner is an AWS Architect here at Version 1.
