Proactive network maintenance (PNM) has become a cornerstone technology within the Data Over Cable Service Interface Specification (DOCSIS®), providing tremendous benefit to cable operators and their customers. From adaptive equalization to full band capture, a rich and extensive data model exists to proactively maintain our valuable networks and reduce operational costs. However, due to the complexity, financial and cultural barriers, many operators have been unable to gain traction with implementing such systems. This paper will examine PNM capabilities, weather information, artificial intelligence, machine learning, operational practice and financial implications to provide a meaningful approach for implementation.
Physical networks that are exposed to the environment are subject to environmental influences which can affect their performance, especially, but not limited to, coaxial and HFC networks and also including fiber optic, satellite, and wireless networking solutions. This is due in part to physics and the simple fact that physical things respond to environmental conditions in predictable ways. Some of the most common factors that influence these networks are atmospheric conditions such as heat, cold, wind, rain, snow, humidity, and freezing. The core premise of this work is to correlate the predictable nature of weather and seemingly unpredictability of weather related outages. While it is generally accepted that these things affect the network, very little work has been done to quantify in an empirical way. Thus, having predictable correlation with faulty network elements allows for operators to proactively prioritize and repair the problems before they impact customers.
Further, by understanding the environmental influence throughout the life span of a network, better decisions can be made about design and construction. This allows operators to evaluate the true cost of ownership over the entire life of the network including support and maintenance, which are generally very expensive.