What is AI in networking?

What is AI in networking?

The key AI for networking technologies include:

• Machine Learning (ML): A subset of AI that enables systems to learn from the data and improve their decision making and performance over time.
• Deep Learning (DL): A subset of ML that uses neural networks to model and understand complex patterns in large datasets providing accurate image and speech recognition and natural language processing. This is particularly effective for analyzing unstructured data.
• Natural Language Processing (NLP): A field of AI that enables machines to understand, interpret, and generate human language. This enhances data analysis through sentiment and intent recognition, for increased efficiency and accessibility.
• Generative AI (GenAI): AI that learns the patterns and structure of input training data, then generates text, images, videos, and other new data with similar characteristics.
• Agentic AI: Leverages intelligent, self-learning agents that can reason, collaborate, and act across domains. These agents function as domain experts, breaking down complex problems into manageable subtasks that are delegated and resolved autonomously. Agentic AI supports intelligent analysis and decision-making, minimizing human involvement.
• Classification AI: A type of machine learning that categorizes data into predefined classes or labels. This process involves training a model on a dataset.
• High performance data warehousing: Consists of vast volumes of structured and unstructured data generated from various sources, which can be analyzed to uncover patterns, trends, and insights for informed and actionable decision-making.

AI provides the computational power and infrastructure needed to process massive amounts of data and perform complex calculations. Without advanced technologies, AI systems would be unable to learn, adapt, and deliver meaningful insights, limiting their effectiveness across various applications.

AI for networking helps create intelligent, adaptive networks that anticipate and address demands and threats through its a transformative ability to automate management, optimize performance, and enhance security. It is gaining traction for several compelling reasons: 

• Adds networking expertise: AI provides recommendations and insights, leveraging a substantial dataset that encompasses a wide variety of customers and networking environments, and can support networking professionals who may lack specialized training.
• Improves observability: AI helps contextualize alerts from a vast amount of network data, helping network operators reduce their Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR).
• Enables scale: As networks become more distributed, AI models are trained to sift through data faster identifying optimizations and recommendations, so networks can scale more quickly without taxing IT resources.
• Augments security: AI can detect anomalies and potential threats more effectively than traditional methods, helping security teams understand scope and impact and providing an extra layer of security in today's complex digital landscape.
• Minimizes problems: AI's predictive capabilities help to proactively identify potential issues before they escalate, minimizing downtime and service disruptions.
• Decreases cost: AI can lower operational costs associated with network management by automating routine tasks and improving efficiency.

Advanced AI techniques are transforming networking in several impactful ways. Here are some examples:

• Support chatbots: AI-powered chatbots can assist in troubleshooting network issues, providing operators with instant support and information.
• Peer comparisons: AI can automatically identify network coverage and performance gaps at comparable sights that are performing better and gain insights to address growing user experience demands.
• Closed loop configurations: AI is used to control processes and maintain desired outputs to allow for continuous monitoring with adjustments based on real-time data.
• Traffic management: AI algorithms analyze network patterns, optimizing bandwidth allocation and routing decisions to improve performance and reduce latency.
• Anomaly detection: AI systems monitor network behavior to identify unusual patterns that may indicate security threats, enabling faster incident response.
• Capacity planning: AI helps organizations anticipate future network demands by analyzing trends and facilitating better resource allocation and investment decisions.
• Load balancing: AI can dynamically distribute workloads across servers and resources to ensure optimal performance and prevent overload.
• Quality of Service (QoS): AI enables more sophisticated QoS mechanisms, prioritizing critical applications and ensuring that they receive the necessary bandwidth.
• Integration with IoT: AI enhances IoT device management, optimizing their connectivity and performance within the network.
• Network security: AI-driven security tools analyze vast amounts of data to identify vulnerabilities and potential breaches, improving the overall security posture.

Without the right AI strategy, it’s hard to keep up with today’s stringent network requirements. Here are several components that an AI solution should include:

• The right data: AI requires large volumes of high-quality, diverse data over time to builds its intelligence. Also, labelling data with domain-specific knowledge helps train AI models. For example, design intent metrics, which are structured data categories, can be used for classifying and monitoring network user experience
• The right response: Good AI in networking solutions should provide accurate insights in real time, reducing alarm fatigue by prioritizing issues and providing recommended actions for remediation. To provide the right response, an AI engine uses various AI techniques, collectively referred to as a data science toolbox, to process that data. Supervised or unsupervised ML and DL such as neural networks should be employed to analyse network data and provide actionable insights.
• The right infrastructure: A robust and scalable infrastructure is required to collect and process data and provide an insightful response. Cloud-hosted processing provides a reliable and agile infrastructure for data collection and processing that can scale to meet AI workload demands now and in the future. 

HPE Networking is uniting two market leaders with best-in-class AI networking technologies, HPE Aruba Networking Central and Juniper Mist, to deliver a next generation, AI-native experience. The strategic vision is to converge these technologies into a single platform built on:

• A common AI engine.
• A unified AIOps framework.
• A centralized cloud architecture.
• Streamlined operations.

While we’re still early in this journey, no matter your current deployment path, HPE Aruba Networking Central or Juniper Mist, HPE Networking is dedicated to delivering a seamless transition and long-term value.