But AI has the potential to improve networks. The new AI network concept combines multiple technologies to automate network operations and help manage networks to improve availability and performance. The model can configure, monitor, troubleshoot and secure the network, manage incidents and provide detailed recommendations and countermeasures.
What exactly is AI networking, how does it work, and what are its main use cases? Let’s learn more about this technology that promises to revolutionize network operations.
Day 2 work for reliability, efficiency and performance
AI networking deeply integrates AI into networking infrastructure to automate numerous processes and improve efficiency, adaptability, performance, speed, latency, and other critical factors. The term AI networking was first used by Gartner in 2023, but the concept also refers to autonomous networking, intent-based networking, self-driven or self-healing networking. It has existed for a long time under names such as healing networking.
There are many stages in a network, from day 0 (initial planning) to day N (end of life). AI networking mainly deals with day 2 operations (continuous maintenance), but it is highly likely that its application will gradually expand to day 0 and day 1 (network development and deployment) functions in the future.
For Day 2, allocate resources, identify and quickly resolve (and predict) network issues, centralize issue identification, automate recommendations and responses, resolve low-level support issues, and reduce problem ticket false positives through confirm-reject analysis. AI can be used for various functions such as:
The ultimate goal is to further increase network stability, efficiency, and performance. Ultimately, the network’s autonomy and self-healing capabilities (the ability to resolve problems without the need for human intervention) may also be strengthened.
Gartner predicts that the proportion of companies using AI to automate day 2 operations will reach 90% by 2027, up from just 10% in 2023. Additionally, Gartner says AI networking can already lower operational management costs by 25% by improving troubleshooting and network availability and reducing support calls.
“AI networking will improve network availability, performance, and operational efficiency,” Gartner analysts Jonathan Forrest, Andrew Lerner, and Tim Zimmerman wrote.
Key Components of AI Networking
The use of AI/ML in network management is not new. For example, AIOps for IT operations is a common way to use automation to improve broader IT operations.
AI networking specializes in the network itself and encompasses several areas including multi-cloud software, wired and wireless LAN, data center switching, SD-WAN, and managed network services (MNS). In particular, the rapid rise of generative AI has brought AI networking to the forefront as executives rethink all aspects of their business, including networking.
AI Networking leverages several advanced technologies listed below to automate processes and monitor networks.
Key use cases for AI networking
Let’s look at some use cases of AI networking. Here’s what an AI-based network can do:
- It analyzes traffic in real time to ensure smooth operation of the network. It is especially useful for businesses with high traffic that need to ensure fast, reliable access and reduce bottlenecks.
- By predicting future demand through usage trend analysis, it supports capacity planning, resource allocation, and maintains optimal network performance and flow. AI can assess network health through performance trends and compare these assessments with industry peers.
- Perform long-term predictive modeling to determine when network outages or performance degradation may occur. They can also identify latency issues or congestion and take steps to reroute traffic, expand infrastructure, or otherwise distribute network resources.
- Optimize IT service management (ITSM) by handling basic troubleshooting (level 1 and level 2 support) such as password resets or simple hardware faults. It also helps eliminate false positives and identify higher-level problems that require human intervention.
- Improve threat response, identify and evaluate security incidents, and recommend actions to resolve them. Augmented by a zero-trust configuration, AI can identify and classify devices on the network, analyze traffic, logs, and user behavior to identify suspicious areas and raise alerts when cyberattacks, breaches, or attempts to do so occur.
- Customize the network experience for different user groups to meet specific needs and improve productivity. This is especially important in large environments with diverse user groups.
- Track IoT endpoints to help with deployment, maintenance, and troubleshooting.
- Automates policies by analyzing traffic flows (protocol, port number, origin and destination) to allow or deny specific interactions between users, devices and apps.
- Improve lifecycle management by assessing devices and ensuring that all devices’ software is up to date. It can find potential configuration vulnerabilities or issues and make suggestions for upgrades.
Challenges in AI Networking
Of course, there is no technology without challenges. Especially with new concepts, there are always problems related to inflated expectations, overhyped features, and excessive expectations. Additionally, new technologies always come with concerns about cost.
No matter how amazing and innovative a technology is, AI can also be wrong. Giving incorrect recommendations can result in overly complex network configurations, leading to outages and other problems. This can be caused by incorrect prompts or poor quality, inaccurate or irrelevant training data.
New technologies also require new skills (e.g., knowing how to give prompts and the output that results from a prompt), so companies must invest time and resources to upskill or reskill their employees. Cultural participation is equally important. This is because workers may be risk averse, distrust AI, fear that AI will replace their jobs, or simply not interested in learning new skills.
How to Start an AI Networking Strategy
AI networking solutions come in various forms. It can be integrated into an existing AIOps platform in a horizontal approach, provided as a feature within a network solution vendor’s platform, a function that can be run independently across multiple vendors, or as a managed network-as-a-service (NaaS). may be provided.
To achieve the best performance, it is very important to choose the architecture that suits your company. Tools must integrate within existing systems, support use cases from Day 0 to Day N, and provide scalability as the network grows.
Before choosing a specific solution, you must first have a clear understanding of what your network needs. Assess your current network infrastructure, understand its challenges and requirements, and identify areas where AI can help.
Gartner suggests starting small and performing PoC testing before using the tool in a production environment. You should test the model’s accuracy by acting on its recommendations and predictions in a sandboxed environment, then apply automation as the system proves itself and builds user trust.
Gartner advises identifying which type of AI networking system (NaaS, AIOps platform, solutions vendor, multi-vendor, or managed service provider) is best for your business based on your existing resources and requirements. We need to determine whether the solution provider’s operating model is MNS or DIY, single vendor or multi-vendor, and ask them to provide detailed information about the tools and their functions and a clear timeline from implementation to the next few years.
NaaS company Nile told executives to invest in systems that collect and process data efficiently and are regularly retrained. To combat cyber threats, you must also ensure that your solutions comply with security standards and compliance requirements. Additionally, implementation costs, which may vary from company to company, must be identified and prepared for.
Equally important is giving IT teams the tools they need to act on AI recommendations and assess how network operations will fundamentally change. Finally, the cost savings and benefits to network availability, performance, and resource efficiency must be calculated to justify adoption.
conclusion
AI networking has the potential to transform and modernize IT networks. There are many opportunities, but also important considerations and challenges. As with all skills, you need to take a strategic approach and reap maximum benefits through careful iteration, teaching and learning, and continuous upskilling.
editor@itworld.co.kr
Source: www.itworld.co.kr
