Building robust machine learning (ML) model for content delivery network (CDN) capacity prediction

Global IP traffic is expected to increase threefold. Also, the data volume of global CDN traffic is increasing exponentially.

This exponential increase in CDN traffic is putting a lot of pressure on DSPs to find new methodologies for optimizing existing capacity and accurately predicting future capacity requirements. Traditionally, capacity planning was mostly a manual process that used basic statistical tools to collect data and set an alert on a static threshold.

However, with the big bandwidth growth and network traffic becoming extremely dynamic, these traditional approaches are destined to fail. Using traditional approach in current landscape can lead to incorrect capacity forecast conclusions. With this, DSPs often run out of capacity due to the pressure of increasing data consumption and changes in consumption patterns that are not identified during capacity planning.

Network planners need to confront these challenges before it impacts the customer experience. This insight talks about how digital service providers (DSPs) can build a robust machine learning-based capacity modeling tool to manage current capacity more effectively and predict future capacity requirements more accurately.

Authors:

  • Suneel Musunuru
  • Sumit Thakur

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