The Next Steps for SD-WAN Automation
Software-defined wide area networking (SD-WAN) has offered a way to reduce the complexity of managing the bandwidth and performance requirements of cloud solutions, while reducing costs with alternative pathways to multi-protocol label switching (MPLS). But the path to network optimization is through SD-WAN automation, fueled by artificial intelligence (AI) and machine learning.
Currently, AI is equipping SD-WAN with the ability to monitor security and identify false positives through machine learning. AI is expected to continue to improve to provide deeper data and network insights as it seeks to better understand trends and the impact of those trends over time.
There is already some automation that allows for a certain level of optimization of the network. For instance, real-time path selection based on business policy is automated, but network engineers still make those policy decisions based on reporting and analysis. The next level would equip machine learning to make these decisions based on immediate data collection.
AIOps Offers Automation Opportunities
AI for IT operations (AIOps) will offer a way to achieve better performance and little downtime, with AIOps able to make changes on a 24/7 schedule. An automated virtual assistant will be equipped to improve user experience, and SD-WAN providers will use DevOps to integrate development and operations so that software features can go to market more quickly. All of these activities can be improved through AI automation.
SD-WAN will also get better at equipping DevOps teams to access provider application processing interfaces (APIs) to add features in their platforms to address certain business requirements.
Even on its own, SD-WAN automation offers benefits – but API integration can make it possible for all elements of SD-WAN to communicate with each other. This allows for real-time, automated changes to WAN edge devices and across cloud platforms. When all elements of the network are in communication, automation can be applied across systems.
Automation to Improve Accuracy
Automation also improves deployment experiences by removing human error. For instance, in the past, MPLS deployments have had performance issues related to the quality of service (QoS) configuration; but with automation, QoS can be added correctly and evolve with AI capabilities.
Machine Learning
IT teams can use machine learning to improve business outcomes and user experience. For instance, if a configuration is causing issues, machine learning identifies the problem and can be tasked with either notifying IT or automating a correction.
As it gathers data, an SD-WAN automation equipped with machine learning can identify patterns to determine the necessary action, and eventually the solution can make decisions even if the data has never been encountered.
SD-WAN automation is just beginning to emerge, but it will take a networking approach that already delivers significant benefits and will introduce more cost-savings and performance advantages. For more information about SD-WAN and leveraging this technology for your network infrastructure, contact us at One Connect.