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Artificial Intelligence (AI) is rapidly changing many aspects of the way we work, from how we interact with technology to how businesses operate. This guide will explore what is AI networking and AI for IT operations (AIOps) and how is it modernizing and simplifying network operations in branch and campus environments.
AI networking is the intersection between AIOps and enterprise networking systems. It involves using AI technologies to automate, optimize, and secure network operations. The term AI networking was coined by Gartner in 2023, which incorporated existing monikers such as intent-based networking and autonomous networks, to capture the scope of Day 0 through Day N networking monitoring, troubleshooting, remediation, and optimization.
AI can be implemented in two ways when it comes to networking. There is “Network for AI” and there is also “AI for Networking”. While the differences are subtle, the former is concerned with creating a network infrastructure that is built for AI; while the latter is focused on implementing AI to automate and optimize the network infrastructure.
Building a network infrastructure specifically for AI is no trivial matter as the critical factor for training any AI is minimum latency and maximum connectivity. The engineering and planning required to bring multiple AI clusters together to connect and extend those clusters to inference nodes to train AI models exponentially increases in complexity with additional data points. Even the most complex traditional enterprise infrastructure can feel less daunting in comparison.
AI for network infrastructure, on the other hand, represents a paradigm shift from traditional methods of manual network management to intelligent, automated network operations. An AI-driven system will continuously monitor the network and dynamically distribute network traffic based on real-time conditions. It makes quick and efficient routing decisions based on factors such as network availability, latency and congestion, ensuring that the most critical applications always receive the necessary bandwidth. AI-driven networks can self-configure, self-heal, and self-optimize, reducing the need for constant manual intervention and ensuring consistent performance.
Traditional network management relies heavily on reactive measures, whereby issues are often addressed after they have occurred. It is typically characterized by chronic alert notifications and support ticket management. AI supplants these traditional modalities by continuously analyzing network traffic patterns, automating workflows, minimizing configuration errors, and expediting resolution times. It can detect anomalies in real-time, predict potential problems, and take preemptive actions to mitigate them.
For network support functions at the basic level, AI can handle level 1 and level 2 support issues, prioritizing their importance and intelligently applying corrective actions as needed. AI can help to discern and reduce false-positive support tickets by approving or rejecting them before they are acted upon by network administrators. By handing off only actionable support tickets, AI frees up IT resources and addresses network issues more quickly and efficiently before they can lead to costly downtime or poor user experiences.
AI Networking is a subset under the broader AIOps umbrella and focuses on Day N of managing and optimizing an enterprise network. AIOps is a framework that combines big data, machine learning, and AI technologies to enhance and optimize IT operations.
AIOps starts with Days 0 and 1 of planning, design, and deployment which includes defining business strategies and outcomes. In Days 2 and N, automation, remediation, and optimization, AIOps platforms analyze vast amounts of data from various IT and network operations tools and devices to identify patterns, detect anomalies, and automate responses to incidents. This approach improves the efficiency and reliability of network operations, reduces manual effort, and enhances overall performance.
AIOps applies many AI capabilities in order to automate and simplify network operations, eliminating manual workflows. Here is how AIOps works:
AI brings numerous benefits to networking, transforming how networks are managed, optimized, and secured.
AI can analyze large amounts of data and intelligently adapt network configurations based on real-time traffic usage. These algorithms can identify patterns and anomalies that can cause potential issues and perform corrective actions before they degrade performance. AI can also identify latency issues, bottleneck areas, and congestion zones. It continuously monitors workloads and resource utilization, and then prioritizes network traffic based on application needs. This ensures that network capacity is used efficiently and Quality of Service is optimally maintained.
Security is a critical aspect of network management, and AI can significantly enhance network security. AI automation reduces the level of manual configuration, and potential misconfigurations, needed to secure a network thereby reducing compliance risks and critical system outages. AI algorithms can continuously monitor network traffic for anomalies and potential threats.
By analyzing historical data alongside monitoring real-time traffic data, AI can identify suspicious activities or known patterns that might indicate cyberattacks. If a threat is detected, AI can triage and automate incident responses to contain the damage or enable rapid recovery. For instance, it can block malicious traffic, remove infected files, and update firewalls. AI can also create and deploy security policies as needed for compliance with industry standards.
AI automation reduces the need for manual intervention in network management. Routine tasks such as configuration, monitoring, and maintenance can be automated. Since AI is continuously monitoring the network, it can quickly identify issues in the network and deploy solutions when possible. AI can also compare historical and current network patterns to help identify any abnormalities in performance and recommend solutions before they become costly critical issues.
AI can also be deployed to manage basic level 1 and level 2 networking issues. By triaging support issues and handing off only critical issues to IT staff, AI eliminates unnecessary manual responses for most network issues, thereby reducing operational costs and improving efficiency.
Since AI can analyze historical and real-time data, it is capable of intelligently managing resources and dynamically scaling the network based on predicted demand. It assesses demand patterns and adapts the network to efficiently handle increasing workloads with minimal disruption. AI’s ability to analyze and identify bottlenecks allows it to strategically allocate resources and organically grow without significant manual intervention. This flexibility not only enhances network performance and user experience, but also future-proofs networks, aligning them with the ever-changing landscape of digital connectivity.
AI impacts various stages of the network lifecycle, from planning and design to management, maintenance, and continuous optimization.
In the planning and design phase, AI helps create optimized network architectures by analyzing historical data and predicting future requirements. AI can simulate different network configurations and scenarios to identify the best design for performance and reliability. This approach ensures that the network is designed to handle current and future demands efficiently.
AI automates routine tasks throughout network management and support, such as configuration changes, monitoring, and troubleshooting. AI-driven systems can detect and resolve issues in real-time, reducing downtime and improving network reliability. Automated support processes also ensure that network configurations are consistently applied, reducing the risk of human error.
AI continuously optimizes network performance by analyzing data and making adjustments in real-time. This ongoing optimization ensures that the network remains efficient, responsive, and capable of meeting evolving business needs. AI can dynamically allocate resources, adjust configurations, and prioritize traffic based on current conditions, leading to better overall performance.
AI networking and network automation are closely related, as AI technologies drive automation by providing the intelligence needed to make informed decisions. Automation in networking involves using software to perform routine tasks, such as updating security patches or backing up data, without human intervention. AI enhances automation by enabling predictive maintenance, automated troubleshooting, and dynamic resource allocation. This makes networks more self-sufficient and resilient, reducing the need for manual management.
AI-ready data models are frameworks designed to leverage AI algorithms for predictive analytics and network optimization. These models are trained on a wide range of datasets that are already present on the network, such as bandwidth usage, packet loss, increased latency, decreased connectivity, jitter, and throughput. By analyzing these data points across multiple centralized Network Operations Centers (NOC), AI can intelligently identify patterns and make accurate predictions.
In predictive analytics, AI models analyze historical and real-time data to forecast future network behavior. This helps with anticipating potential issues and making proactive adjustments to prevent downtime and improve performance. Predictive analytics can identify trends and patterns that may not be obvious through traditional analysis, allowing for deeper insights into network operations.
Additionally, AI models optimize resource allocation by predicting demand and maintenance needs, reducing waste, and improving operational efficiency. This adaptive approach not only mitigates risks but also capitalizes on new trends, effectively future-proofing businesses against shifting market conditions.
AI models optimize network performance by continuously analyzing traffic patterns and resource utilization. They can dynamically adjust configurations to ensure optimal performance, reduce latency, and improve user experience. AI-driven optimization can also balance loads across network resources, preventing congestion and ensuring network capacity is used efficiently and maximally.
While adoption for AI networking and AIOps continues to grow, AI technologies are rapidly improving in a variety of use cases.
AI can automate routine network management tasks such as making configuration changes, monitor network health, and proactively address issues. This reduces the burden on IT staff, ensuring networks are managed efficiently and consistently.
AI algorithms continuously monitor network traffic for anomalies that may indicate security threats. By analyzing patterns and behavior, AI can detect suspicious activities and respond in real-time, enhancing network security and reducing the risk of breaches.
AI networking can improve network traffic by prioritizing critical applications and ensuring optimal bandwidth allocation. By analyzing real-time data, AI can identify high-priority tasks and allocate resources accordingly, preventing bottlenecks and ensuring smooth performance for essential services. This dynamic adjustment not only reduces latency and packet loss but also enhances the overall efficiency of the network. AI can also adapt to changing network conditions, continually optimizing traffic flow and resource distribution resulting in a more reliable, performant network.
Based on network conditions, AI can predict a user’s internet performance, allowing the system to dynamically adjust bandwidth capacity based on which applications are in use at specific times. This ensures that critical applications always receive the necessary bandwidth and low latency they require when needed. As a result, users experience a smoother, faster, and more reliable network performance.
AI deployed across a network allows the system to quickly and efficiently aggregate data and offer real-time analytics on user interactions and network performance. By learning how a series of events are correlated to one another, AI can help predict likely scenarios before they happen and alert IT staff with suggestions for corrective actions.
Deciding what role AI can play in your network operations involves assessing your current network challenges and goals. Consider the following factors:
If your network is large and complex, managing it manually can be challenging and time-consuming. AI networking and AIOps can simplify management by automating routine tasks and providing real-time insights into network performance.
If your network experiences frequent issues and downtime, AI networking can help by detecting and resolving problems proactively. AIOps can further enhance reliability by automating incident responses and root cause analysis. In deciding how AI can address network performance and reliability, explore whether a Network-as-a-Service (NaaS) solution that offers a fully managed network, powered by an AI-driven platform, can not only solve network issues today and tomorrow, but offer guaranteed network performance.
AI networking and AIOps can reduce operational costs, improve network performance, and enhance security. These benefits can lead to better overall efficiency and a more resilient network infrastructure.
Align your decision with your strategic IT goals. AI networking and AIOps can be highly advantageous if your aim is to improve network reliability, performance, and security while reducing manual intervention and operational costs. With AI networking, you can more efficiently utilize limited IT resources and have a better understanding of when to escalate issues and deploy IT staff.
Evaluate access to AI expertise, ease of implementation and interoperability of AI networking and AIOps solutions with your existing infrastructure. Not all network ecosystems are compatible with certain AIOps solutions, and even a seemingly simple AI network upgrade may require changes to infrastructure and monitoring tools. A NaaS solution would alleviate this complexity, and at the same time ensure the entire network infrastructure is future-proof with continuous delivery of new technology as the usage of AI continues to grow.
AI networking is rapidly changing the ecosystem of network operations and security. By leveraging machine learning, deep learning, and automation, AI networking enhances performance, reduces operational costs, and provides robust security measures. As technology continues to evolve, AI networking will play an increasingly vital role in simplifying complex network environments and ensuring seamless connectivity.
AIOps can further extend the capabilities of AI networking by providing comprehensive monitoring, predictive maintenance, and automated incident response. Businesses that embrace AI networking and AIOps together will be better positioned to meet the demands of the digital age and stay ahead of the competition. Especially critical for IT and business leaders, understanding AI networking is crucial for navigating the future of network technology.
Transform your network infrastructure with AI. Talk to a Join NaaS expert today and explore how our NaaS solutions simplify networking and ensure performance, reliability, and flexibility.Network-as-a-Service (NaaS) delivers an integrated solution for advanced network management, equipping organizations with the capabilities necessary to ensure scalability, agility, cost-efficiency, and enhanced security.
For IT managers, network administrators, and CIOs, managing a robust and efficient network is crucial. Whether you view networks as a cost center or business enabler, any network that’s minimally supported and managed will quickly turn into a business disabler.
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