More and more companies are leveraging the synergies between artificial intelligence and networking. As user devices and the data they generate proliferate, enterprises are increasingly relying on artificial intelligence to help manage vast network infrastructures.
By 2024, 60% of enterprises will have artificial intelligence infrastructure, which will require broader automation and predictive analytics for network troubleshooting, incident prevention, and incident correlation.
Artificial intelligence is becoming increasingly common as enterprises try to leverage the resources their IT departments have to manage increasingly complex networks. Operations that network administrators used to perform manually are now largely automated, or are moving towards automation.
However, no matter how large the enterprise is, using artificial intelligence cannot avoid network outages. Facebook experienced a major outage in October 2021, which the company blamed on a router reconfiguration error. AWS also experienced an outage in December 2021, which it blamed on a network scalability error.
Although AI is complex and can do many things for a network, it is not foolproof. This emphasizes the continued importance of human intervention in networks.
Artificial intelligence, more specifically the application of machine learning, helps network administrators ensure network security, troubleshoot, optimize and plan network development .
In the era of home office and work from anywhere, the proliferation of network endpoints has expanded the attack surface of the network. To remain secure at all times, the network should be able to detect and respond to unauthorized or compromised devices.
AI improves the process of authorizing devices to enter the network by setting and continuously enforcing quality of service and security policies for devices or groups of devices. Artificial intelligence automatically identifies devices based on their behavior and continuously enforces the correct policies.
AI-powered networks can also detect suspicious behavior, off-policy activity and unauthorized devices accessing the network faster than humans. If an authorized device is indeed compromised, the AI network provides context for the incident.
Device classification and behavior tracking can help network administrators manage various policies for different devices and device groups, reducing the possibility of human error when introducing new authorized devices to the network. It also helps them detect and troubleshoot network issues in a fraction of the time.
Before AI-driven networking, network operations needed to identify network issues by examining logs, events and data from multiple systems. Not only does this manual work require time and extended downtime, but it is also prone to human error. The sheer volume of data involved in today's networks makes it impossible for any NetOps team, no matter how large, to sift through event logs to identify and fix network issues.
Now, AI can not only allow networks to self-correct problems for maximum uptime, but can also provide NetOps with actionable recommendations for action.
When an issue occurs, the AI-driven network uses data mining techniques to sift through terabytes of data in minutes to perform event correlation and root cause analysis. Event correlation and root cause analysis help quickly identify and resolve issues.
Artificial intelligence compares real-time and historical data to find relevant anomalies to begin the troubleshooting process. Examples of relevant data include firmware, device activity logs, and other metrics.
Artificial intelligence networks can capture relevant data before an incident occurs to aid investigation and speed up the troubleshooting process. Data from each event helps machine learning algorithms in the network predict future network events and their causes.
In addition to detecting and learning from network failures, AI automatically repairs failures by leveraging the network's rich historical database. Alternatively, it relies on this data to make precise recommendations on how network engineers should approach the problem.
Artificial intelligence capabilities simplify and greatly improve the troubleshooting process. Artificial intelligence reduces the number of tickets IT has to handle, and in some cases, it can solve problems before end users or even IT notice them.
Keeping your network up and running and secure at a baseline is one thing, but optimizing it is another. The process of continuously optimizing the network makes the end user happy.
Wireless connectivity standards continue to evolve in terms of speed, number of channels, and channel bandwidth capacity. These standards are beyond what any traditional NetOps program can handle, but not much for an AI-infused network.
Network optimization includes network monitoring, routing traffic, and load balancing. This way, no part of the network is overburdened. Instead, by distributing traffic more evenly throughout the network, the network is able to efficiently deliver the best quality of service possible.
Today’s networks require self-optimizing AI networks based on network data from real-time events. For example, with deep learning, computers can analyze multiple data sets related to the web. Based on this data, the network's recommendation engine checks the policy engine and makes intelligent recommendations to enhance existing policies.
On the one hand, these recommendations meet quality of service baseline standards despite changing circumstances, such as traffic spikes in specific geographic areas or user devices. The recommendation engine might suggest switching to idle assets or redirecting traffic over a longer path to alleviate congestion.
At the same time, the recommendations adhere to the network's baseline operational constraints, such as prioritizing phone calls and text messaging over video streaming.
The network will re-optimize the device itself based on recommendations. Self-optimizing networks maximize the use of the network's existing assets, guiding it how best to operate with limited resources while ensuring compliance with service level agreements.
Enable users to have the best possible network experience through observability and orchestration of artificial intelligence-driven networks.
Considering the development of 5G networks, AI will have the greatest impact in network planning to provide new services or expand existing services to underserved markets.
A 2018 Ericsson report found that 70% of global service providers reported that artificial intelligence had the greatest impact on network reliability. Following closely behind, reliability, network optimization and network performance analysis are two other areas where 58% of respondents said AI is gaining attention.
Using artificial intelligence for network performance analysis enables communications service providers to accurately predict the needs of their networks so they can better prepare.
For example, artificial intelligence can be deployed to improve the geolocation accuracy of supplier networks. Doing so can provide critical information that helps providers assess service quality in specific areas. This information, in turn, informs future network upgrade plans.
AI also comes into play when trying to identify underserved market segments. It helps distinguish served and unserved markets from satellite imagery.
Artificial intelligence provides enterprises, especially communications service providers, with a competitive advantage by helping them identify and act on strategic opportunities.
Infusing artificial intelligence into networks provides businesses with many benefits, including:
Given the many benefits of artificial intelligence networks, they are sure to continue to grow in today’s enterprises. Artificial intelligence plays an increasingly important role in managing networks that are becoming increasingly complex.
However, the fear that artificial intelligence will replace networking professionals is a noteworthy but ultimately unnecessary concern. Networks still require humans to validate and occasionally augment AI capabilities by handling the discrepancies between network problems and proposed solutions generated by the system.
Help the machine when it cannot provide a solution with a high degree of confidence.The above is the detailed content of What is network artificial intelligence. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
