Avoiding "Hot Potato" Problems in Internet Service Providers

Internet service providers (ISPs) strive to provide the best possible services to their customers. Service outages, or incidents, due to technical failures are inevitable, so the aim of ISPs must be to respond as quickly as possible to error notifications. However, services may rely on thousands of devices and components that are interconnected and managed by different teams (network administrators, technicians, etc.). Identifying the team to which an incident ticket should be assigned becomes a tedious task that slows down recovery time.In this paper we focus on the problem of finding the right team when an incident occurs. We group teams into logical team groups and use machine learning models that we train on previous resolved incidents to predict the most appropriate team group from a failure description. Using a large dataset from a national ISP and telecommunication company, we demonstrate that, even with a small amount of information available at the beginning of the incident, machine learning models can achieve an accuracy of 88.52% and an F1 score of 90.17%. With more complete information about the incident, the accuracy and F1 score increase to 90.52% and 91.7%.