Traditional network architectures, originally designed for predictable, asymmetrical traffic, are in need of retraining as new AI applications are gaining popularity in enterprise environments. Legacy systems were not built to handle the high-speed, bursty, and unexpected visitors patterns that these new loads introduce. With more AI-enhanced and AI-generated solutions available, this change presents challenges for network functionality, stability, and asset allocation.
As a result, businesses now have a double need: maintain continuous program performance while adapting to a growing demand for AI-driven information flows. In fact, in 2024 alone, there were 39 exabytes of network traffic based on Omdia’s files, which was released by Network World. As businesses continue to use more technology, insights, and AI-powered options, that number is anticipated to increase.
A better system that can detect what’s happening in the system, make informed judgments right away, and adjust before system performance issues arise is needed to solve this issue.
This article explores how VeloCloud’s AI-powered community infrastructure helps businesses meet today’s difficulty while meeting today’s system performance demands.
Dynamic Multipath Optimization with AI at the helm
In today’s cross and multicloud settings, network customers no longer moves in predictable designs. For example, in a normal business setting, applications span public and private skies, users connect everywhere, and traffic conditions change every second. Add to that the growing number of AI-generated and AI-powered data moves, and it becomes clear that fixed backup plans and dynamic route rules no longer work.
This kind of business networking atmosphere is made possible by VeloCloud’s Dynamic Multipath Optimization TM. It works by regularly monitoring each obtainable WAN link, including broadband, 5G, MPLS, dish, and more, and comparing their performance against key performance indicators like overhead, packet loss, latency, and throughput. Finally, it uses AI to make router adjustments in real time. This means that if one path begins to trail, the system silently reroutes traffic to a better-performing choice without waiting for limits or outages. Depending on current networking conditions and application requirements, it occurs quickly. It seems as though your car’s GPS updates your way every time there is an incident, not just the moment the flow of traffic starts to change.
Better Prioritization and Better Customers
It becomes crucial for organizations to ensure that the most important workloads receive the right kind of customers at any given time without having to rely on IT teams to step in as conceptual AI, agentic AI versions, and latency-sensitive applications grow in size.
Many businesses are launching AI-focused social initiatives in reply, as VeloCloud’s State of the Enterprise Edge report demonstrates.
These efforts are in line with recent Opengear research that found that 57 % of network engineers anticipate that their organizations will invest more than 25 % in AI for network management over the next two to three years. This increase is a result of a growing drive to meet the demands of agentic AI loads operating outside the traditional data center in terms of efficiency, speed, and security.
VeloCloud addresses this yawning space by using its AI-powered network infrastructure, which combines automated real-time plan adjustments with machine learning-based application recognition. It continuously monitors customers, tracks thousands of applications, even those that are encrypted, and automatically allocates speed based on performance requirements and company priorities.
Let’s say there is a worldwide video meeting taking place at one of our global offices. VeloCloud immediately recognizes the prospects, comprehends its significance, and prioritizes it above all other traffic. Picture tasks like system updates and document syncs are slowly prioritized at the same time. The intriguing aspect of this is that no one else has to manually update laws or modify settings. These decisions take place in real time.
That strategy is readily applicable to thousands of users and places. The system seamlessly responds to your needs whether you’re working in a branch office, accessing a design hosted in the cloud, or taking a visit from home.
IT teams gain from fewer manual quality-of-service ( QoS ) adjustments, which free up more time for pursuing bottlenecks.
AI-Driven Networking is supervised and secured.
Enterprises that use AI workloads require both performance and a robust security position and precise control over their networks.
VeloCloud’s VeloRAIN infrastructure improves effectiveness, security, and flexibility for distributed and AI-driven workloads by bringing innovative AI network capabilities to VeloCloud SD-WAN. By incorporating features like AI-driven program monitoring, active application-based tearing, and automatic system operations, it goes beyond simply combining SD-WAN, fog security, and observability. These features enable IT teams to quickly assess how much software usage and performance are consumed, allowing them to modify policies as necessary.
Playtech SSE for VeloCloud, a cloud-based services designed to protect distributed AI software, increases safety more. It incorporates line CASB to manage risks from darkness AI, uses machine learning to identify and prevent emerging threats, and scales encrypted traffic.
Sites that Think Forward Are Available?
With AI at its base, VeloCloud is revolutionizing network performance. Enterprise networks are given the tools to think and adjust in real time thanks to brilliant traffic classification, predicted bandwidth allocation, and active path optimization.
Investigate how we use AI to improve system performance to make sure your business loads run smoothly.
