Artificial intelligence (AI) continues to dominate tech headlines, affecting many industries due to its incredible potential. Network infrastructure is one area where AI’s ever-improving technology has a significant impact.
In this article, we examine how AI-driven network infrastructure works to help you understand how AI and related tools influence network design, maintenance, and management. We also explore related principles specific to voice, video, and unified communications (UC), highlighting how AI-enabled infrastructure enhances these essential services.
Networks are becoming ever more complex. Constantly increasing requirements in bandwidth, resilience, availability, security, and on- and off-premises resources are all becoming intrinsic parts of network design.
An enterprise network can quickly develop into a large, unwieldy entity that becomes difficult to manage and maintain effectively over time. Network automation systems have helped alleviate much of the headache of managing many different facets of the network, but network automation alone is not always enough.
The natural next step in overall network administration and management is to involve AI. AI adds intelligence to network automation systems, allowing them to optimize networks dynamically, intelligently respond to changing behaviors and patterns, and quickly react to potential security threats, all without human intervention.
AI-driven network design and management reduce much of the complexity of network administration. This allows network administrators to operate more effectively as network management systems become more autonomous.
In this article, we highlight the possibilities that AI can deliver in this area. There are real production implementations of AI intelligence within network monitoring and automation systems, but the actual capabilities of each system depend upon the level of integration with AI that each product’s manufacturer has employed.
Although we are not discussing any particular AI-driven network monitoring system, all of the described capabilities and features are available in some form in today’s network automation systems.
Real-time services such as voice, video, UC, and other collaboration-oriented applications rely heavily on the near-flawless operation of networks. We’ve all experienced low-quality, choppy sound or frozen frames in our communication applications, and we know how frustrating that can be. But frustration is the least of it.
Today’s businesses depend increasingly on communications systems for normal day-to-day operations to support the mobile workforce and the hybrid office paradigm, so these systems have become mission-critical. Without functional and effective communications and collaboration tools, businesses can quickly grind to a halt, suffering severe financial consequences and potentially losing market positioning.
Over the past decade, network automation systems have become a significant part of networking and telecommunications.
Many noteworthy proprietary and open-system network automation solutions have been introduced into the industry with exceptional results. These systems are now being enriched with AI-driven solutions, introducing essential intelligence and adaptability for supporting network operations, particularly real-time communication services.
AI plays a specific role within network infrastructure in several specific areas.
Network management is the day-to-day maintenance required to ensure that a network is operating optimally. AI can streamline this process by adding intelligence to automation, providing capabilities like dynamic network device configuration modifications based on real-time demand and adaptive network policies.
The primary focus in AI-driven network management is dynamic and intelligent adaptability. This addresses which mission-critical services receive priority ensures that resources are efficiently utilized, and anticipates potential issues so they can be mitigated before impacting users.
High-quality voice and video services require top performance. QoS and QoE are important components that help guarantee these performance standards. AI can further enhance these through predictive analytics and real-time network adjustments.
One of the most noteworthy capabilities of AI is predictive analytics. When applied to traffic management, historical and real-time data can be used to forecast network behavior, enabling demand forecasting and future capacity planning.
AI can also detect anomalous patterns that may indicate security threats or hardware failures, which may otherwise go undetected. This capability allows for swift notification of administrators as well as quick intervention.
Effective network design is essential for supporting the high demands of key services, including real-time services. AI’s role in guaranteeing flawless network operation for such applications primarily focuses on bandwidth management, minimizing latency and jitter, and ensuring scalability.
Bandwidth allocation and management
Proper bandwidth allocation ensures that voice and video services perform optimally without interruptions. Network infrastructures that leverage software-defined networking (SDN), MPLS, and other complex network technologies can benefit from adaptive and intelligent bandwidth allocation.
Latency, jitter, and packet loss considerations
AI applied to network monitoring and maintenance systems is also of benefit for minimizing latency, jitter, and packet loss, three of the most important network phenomena for real-time communication services. AI’s predictive analytics capability is ideal for anticipating and mitigating such network issues.
AI can detect anomalies and adjust parameters in real-time, ensuring smooth and reliable voice and video transmissions.
Securing voice, video, and UC traffic is essential to protect sensitive communications from interception or unauthorized access. The same intelligence and adaptability that AI bestows upon network management and automation systems can significantly enhance network security as well.
Advanced AI-driven intrusion detection and prevention mechanisms can identify threats and respond to them in real time. AI can analyze patterns in data flow to detect malicious activities that deviate from normal behavior, responding with immediate action against potential breaches and minimizing harm.
AI’s impact is expected to touch a multitude of areas in telecom and networking in the near future. AI’s integration with 5G networks and edge computing is transforming mobile communications. The synergy between AI and next-gen mobile networks enhances UC experiences by providing faster speeds and lower latency.
Edge computing brings intelligence closer to users, reducing latency and improving real-time processing, which is crucial for voice and video applications.
Furthermore, the evolution of AI in networking includes predictive networking and self-healing systems. AI plays a significant role in network virtualization and software-defined networking (SDN), enabling flexible and efficient network management.
Predictive analytics allow networks to anticipate and resolve issues proactively while self-healing systems automatically correct faults, ensuring continuous service availability.
The integration of AI into network infrastructure is transforming the delivery of voice, video, and UC by enhancing efficiency, security, and user experience. By adopting AI-driven network design principles, organizations can ensure scalable, reliable, and high-quality communication services that meet current and future demands.
Embracing these technologies positions businesses to stay competitive in a rapidly evolving digital landscape.
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