AI for Networks


Randal here; I work for INM as a solution architect.
I want to begin a new series today called AI for Networks.

In this article, we’ll provide you some examples and suggestions for how using various machine learning approaches might help your network.

Network Capacity Planning

My first piece will concentrate on capacity planning.

We are aware that every gadget, whether real or virtual, has a certain maximum working capacity. A crucial element for the efficient operation of our networking or data center environment is the provision of a sufficient amount of capacity.

When our environment is large and filled with many devices, a problem occurs. How can we efficiently plan improvements for increased capacity?

Planning Capacity using AI

Here, numerous machine learning methods are put to use. The data from resource consumption monitoring can be used with these strategies. They can automate the process of assessing patterns and spotting potential capacity issues in the future. Our administrators are provided with signals, alerts, and recommendations as a result, enabling them to proactively handle such problems.

AI-based capacity planning: An example

Imagine a basic network configuration. Each switch and link has a maximum capacity, and time series data structures are used to track resource usage. These time series show measured values that were recorded at successive timestamps. Timestamps are typically evenly spaced for fast data processing.

For instance, over a single day, a few days, and a longer time frame, we can monitor connection utilization as a percentage. With time, trends, especially escalating ones, become apparent.

Planning for Capacity Using Historical Data

These historical data can be used by machine learning algorithms to create models and forecast future patterns in resource usage. We can predict when we’ll reach specific thresholds, like an alerting level of 80%, or when capacity constraints may happen, using this pattern. We scan the network and develop a system that notifies administrators when updates are required by applying this method to a variety of time series data points.

Benefits of Using AI for Capacity Planning

We streamline the process of giving indications on which components of our infrastructure require changes and when by increasing automation. This makes planning more exact. Purchasing new equipment is frequently necessary for capacity improvements, which necessitates waiting for delivery and careful integration. With more knowledge, we can better plan these operations, thereby lowering capital expenditures (CAPEX) and preventing expensive last-minute acquisitions.

We also want to avoid issues brought on by insufficient capacity. Numerous apps may experience longer delays and greater packet loss due to traffic forwarding. In addition to helping administrators, such a method lowers stress by giving more predictable insights.

A similar method can be used to monitor CPU, memory, and storage use across servers, virtual machines, and clusters. By utilizing time series forecasting and machine learning algorithms for data processing and anticipation, this promotes smarter infrastructure expenditure.

Anomaly detection is a crucial component. By comparing actual metrics to predicted ones, forecasting and projections help us spot abnormalities in data.

We appreciate you reading this article.

Please post any queries you may have in the comments section.

In the following post, we’ll go into more detail about enhanced alerts, emphasizing quicker root cause detection and filtering out meaningless signals.

Keep an eye out for our future article.