Making sure that you have the capacity required in the network at all times is an art. But with new artificial intelligence (AI) tools, that art can be easier to master. For the network to meet the goals set for the delay, damage, and availability in the network, capacity planning is required — a complex task where big money is at stake if one fails.
Until now, the data that was made available to plan capacity is static historical data. But now it has started to move more to artificial intelligence. By pairing cognitive technology such as machine learning (ML) and AI with advanced data analytics, IT can initiate new and broader predictive insights and make network planning more accurate. Although AI-supported capacity planning is still in its infancy, many suppliers are also starting to incorporate some form of AI into their offerings.
AI boosts network monitoring
Allowing AI to evaluate data from many sources provides better accuracy than conventional network monitoring that strictly looks at link usage. AI also allows modeling of diverse scenarios and links the network performance to used application performance to understand how apps are affected in changed performance scenarios.
The analytics model optimizes over time as it tries to learn the system. AI makes planning more precise as the network nurtures and when new applications and new users are added. Machine learning is also useful in predicting traffic and traffic patterns.
Advanced ML learning algorithms can capture both large-scale and vastly detailed data. The algorithms generate accurate forecast data for each node and identify different time patterns for network usage and network traffic. The result is a more precise assessment of network capacity checks, and this reduces the need for over-utilization of resources.
Being able to detect patterns early in how things would (in the past, present, and future) affect each other provides an opportunity to act proactively to ensure the network’s performance. Sophisticated analytics and predictive models can be used with optimization or simulation technology to create the optimum network structure, and corresponding capacity and resource plans.
The plans can then be tailored for precisely the critical network components that are considered most important. By using AI to analyze traffic designs in the network in different ways, organizations can take advantage in precisely what is running over the network and the total network load.
In the near short term, AI can forecast daily traffic problems at the granular levels, such as network protocols, application, technology, and location, which can keep performance up to the mark. In the long run, the system can achieve ideal capacity planning, predict when short peaks cannot meet, and when full-scale upgrades or advancements are required.
The best starting point with AI-driven capacity planning is to buy technology that has already proven that it works and manage to achieve some success. One such example is the Cisco AI-based network analytics solution that adopts a software-supplied approach to automate and guarantee services across all campus networks, WAN, and branch networks. With technology from an open and expandable platform, Cisco AI-based solution allows you to add value to the network to optimize operations and facilitate business and IT innovation.
At the same time, administrators should be careful to follow solutions provided by AI learning algorithms. They will improve drastically in the coming years as the technology matures. So users must keep patient and allow AI technology to develop before entirely relying on the recommendation by the AI.
Start on a small scale when it comes to data sources and how much to monitor. The data sources must be reliable, and it is also crucial that the right data is used in the right context.
Formulate your data so that you can get into the solution as efficiently as possible and ensure that it provides the network capacity that is appropriate to your goals. Once the pipeline of data has been recognized, and speeds are in line, the AI solution can start monitoring information for precise behaviors.
Finally, when AI starts making recommendations, the IT infrastructure team can create automation policies based on these behaviors.
AI is not for complete automation
A common misconception about how AI can be used to plan the capacity of the networks is that the technology does not require a great deal of resources, especially not human hands. But it is an illusion born by some suppliers that sounds like it is basically enough to install the AI tool, and then it works by itself.
Another common misunderstanding is that companies incline to implement all AI features very quickly. Companies gradually introduce AI tools and focus on the areas where value is most significant. That many parts of future networks will be cloud-based makes this even more critical.
But the biggest misconception is that the AI-based solution will always be right. Until the AI solutions are tested in practical scenarios, it will be necessary for admins to authenticate and question the AI-driven capacity planning solutions.
If your system is short of comprehensive data, or if the data is not reliable, the AI tools will not give a correct picture of your network or the network performance. It is essential to look for providers that offer a complete range of products and services. But, most solutions in the market do not provide a perfect AI network solution for all your needs. Integrating AI solution to your existing network is an evolutionary process, and you should have the right planning to get started.