How AI can improve network capacity planning
It can be said without doubt that a good network connectivity is becoming as important to human survival as is food, water and air. There are several factors that come into picture when deciding on the parameters of a network and crating networks. Artificial intelligence is helping in improving the network capacity planning.
What does network capacity planning do?
Network capacity planning is essentially used to make sure that sufficient bandwidth is provided in the network and thereby allowing the targets of the network service level agreement targets to be met. This is a complex task as it involves taking into mind several factors and the occurrences of error could cause serious financial implications.
The arrival of AI:
AI has helped in the network capacity planning by using data to provide better insights and thus help the decision-making process. Combining data science and cognitive technology which includes Ai and machine learning, smarter insights can be obtained which will be able to improve the network capacity planning accuracy.
How it helps:
AI helps in modelling of the various performance scenarios and combines the network performance the performance of the application, this is then used to determine how applications are impacted in various performance scenarios.
Learn from experience:
The most important feature of AI is its ability to learn from its experience. AI driven machine learning which when applied to network performance allows a network controller to learn from its experience as it continues to improve the network. This can improve the accuracy of the capacity planning when the network is altered or grows by the adding of applications and users.
AI methods can be applied to improve the traffic prediction in the network. It is capable of pattern detection, learning and better decision making. The advancements in the algorithms allows us to provide them with large scale network data as their input and they can generate very accurate forecasts for the demands for each node in the network and also the trends in the network traffic and utilization.
Early detection of the changes in network flow allow organisations to take actions that ensure good network performance. The predictive systems can be combined with optimisation and simulation techniques and this can be used to generate the most efficient network structure and the corresponding capacity and resource plans.
AI systems can process the traffic data in real time and thus improve the network in real time. This can be done by altering the routing and the allocation decisions dynamically so as to ensure the optimal network capacity. AI can interpret several data points and thus is helpful for large organisations which have network spread across data centres, cloud environments and WAN.
These were a few important ways in which AI is going to be helpful in improving the network capacity planning. With the increase in the capabilities of AI the issues that occur in networks can almost be reduced to zero. The combination of AI and ML makes it even better to have optimal network operations.
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