Overcoming the Challenges of SD-WAN Networks with AI and Tunnel-Free Routing

Software-defined wide area network (SD-WAN) is a popular networking solution that offers organizations the ability to connect their branches or remote locations to the central network. However, managing and optimizing an SD-WAN network can be complex due to its increased complexity and associated operational data. To overcome these challenges and optimize network performance, organizations are turning to innovative solutions such as AI and tunnel-free routing.

Increased Complexity and Operational Data in SD-WAN Networks

With the advent of SD-WAN, organizations are able to enjoy the flexibility of having multiple paths and connections that traffic can take on its way to its destination. However, this advantage also comes with a complex network infrastructure that requires careful management and optimization. Managing an SD-WAN network involves handling an abundance of operational data, including traffic patterns, quality of service (QoS), network performance, and more.

Multiple Paths and Connections in SD-WAN

One of the critical features of SD-WAN is the ability to route traffic through multiple connections to optimize performance and reduce latency. However, this feature can also cause a headache for network administrators as they struggle to monitor and control the traffic flow. An SD-WAN network can have multiple paths, including broadband, MPLS, 4G, and satellite links, and ensuring that traffic flows optimally can be a daunting task.

Performance Issues with Tunnel-Based SD-WANs

One of the traditional methods of implementing SD-WAN is through tunnel-based routing, which encapsulates traffic between branch offices and the central network. Although tunnel-based SD-WANs can provide an efficient way to route traffic, they can also cause performance issues. When SD-WAN tunnels become congested, it can result in packet drops, deteriorating network performance and user experience.

SD-WANs and Public Cloud Platforms

SD-WANs have become a popular choice for organizations looking to connect to public cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure. However, connecting to the public cloud using an SD-WAN can be very expensive, as cloud providers charge companies based on the amount of data going to and leaving their clouds. This expense and complexity comes with the added challenge of managing and optimizing the network for cloud connectivity.

Cost issues in SD-WAN cloud connectivity

The added expense of cloud connectivity has left network administrators searching for ways to optimize the network to reduce these costs. The traditional approach of connecting branches to the cloud through a hub-and-spoke architecture using an MPLS connection can be costly and inefficient. Organizations are now looking for alternative solutions that offer faster, more efficient, and cost-effective cloud connectivity.

AI as a solution for managing and optimizing networks

One solution for managing and optimizing an SD-WAN network is to incorporate artificial intelligence (AI) technology. AI can help network administrators make informed decisions about network operations, acting almost like an autopilot for the network. With AI, the technician can ask why the network was not as strong in a particular area, and the router can be quickly identified based on the response.

Benefits of Incorporating AI into Network Environments

By incorporating AI into an SD-WAN network environment, businesses can benefit from automation, machine learning, and predictive analytics. This technology can monitor network performance in real-time, detect anomalies, and react to them immediately. By providing proactive support, AI technology can save network administrators time and money while increasing network performance and optimization.

Tunnel-Free Routing as an Alternative to Tunnel-Driven SD-WANs

Another alternative to traditional tunnel-driven SD-WANs is the use of tunnel-free routing. This innovative solution offers a better way to route traffic between branch offices and the central network, eliminating the network performance issues inherent in tunnel-based SD-WANs. Tunnel-free routing routes traffic based on its characteristics, ensuring that the traffic passes through the most optimal path.

Faster failover and traffic redirection with tunnel-free routing

Tunnel-free routing also enables faster failover as critical traffic can be redirected over an alternative path without disrupting the user experience. This means that even if one connection fails, the other path can pick up the slack, ensuring that data flows smoothly between the branch offices and the central network.

In conclusion, SD-WAN networks offer organizations increased flexibility and multiple paths and connections that traffic can take on its way to its destination. However, managing and optimizing these networks can be complex, expensive, and time-consuming. AI technology and tunnel-free routing provide organizations with innovative solutions that can help eliminate network performance issues, reduce costs, and increase network optimization. With these solutions, organizations can ensure that their important traffic is not disrupted due to traffic latency or an outage that technicians cannot seem to source.

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