Applied Distributed Systems For Large-Scale Telecom Network Optimization

Abstract

Given the continued growth of global telecommunications, the engineering of telecom networks has far outgrown the customary centralized planning architecture. This article covers the application of distributed systems to architecture design, artificial intelligence, real-time processing, and fault tolerance, which are the key operational domains of large-scale telecom network optimization. With the unprecedented growth of mobile subscriptions and mobile capacity under intense pressure from data-based applications, scalable, automated and reliable planning platforms have become a critical challenge for operators. The convergence of big-data technologies, distributed machine learning, streaming observability frameworks, and self-healing infrastructure patterns has made possible a new generation of planning platforms that, at scale and speed, can replace human-centric engineering. The principles and applied patterns discussed in this article are representative of a maturing discipline and relevant to the global telecom community.