Chroma: Learning and Using Network Contexts to Reinforce Performance Improving Configurations

Managing network configuration and improving service experience effectively is essential for cellular service providers (CSPs). This is challenging because of cellular networks' large scale and complexity, the wide variety of configuration parameters, and the performance impact tradeoffs resulting across multiple metrics and geographical locations. This paper focuses on learning and using network contexts to recommend performance-improving configurations. While learning contexts, one must carefully account for the configuration parameter dependency, performance impact confusion that can arise due to co-occurring unrelated changes, and uneven change deployment distribution across locations. We present a new solution Chroma that addresses the above challenges. Using real-world data collected from a large operational LTE and 5G cellular service provider, we thoroughly evaluate and demonstrate the efficacy of Chroma. We successfully trial Chroma on an operational cellular network and highlight its benefits in practical settings.