Editorial

Computational Management Science. 2024. Vol. 21. No. 1. Article 35.

Panos Pardalos, Valery Kalyagin, Mario R. Guarracino

Big data has become an integral part of modern networks. With the increasing amount of data generated by devices, machines, and applications, networks are constantly being challenged to handle and process this data in a timely and efficient manner. The size, complexity, and variety of data in networks are increasing rapidly, which requires new approaches to handle and analyze this data. Big data analytics provides the tools and techniques to manage, process, and extract insights from massive datasets in networks.

One of the key applications of big data in networks is network management and optimization. Big data analytics can help network operators to monitor and analyze network traffic, identify anomalies, and optimize network performance. By processing large amounts of data in real-time, network operators can quickly identify potential issues and take proactive measures to prevent network downtime. Furthermore, big data analytics can help network operators to make informed decisions about network investments, capacity planning, and service delivery, based on the analysis of historical and real-time data. Overall, big data has a significant impact on the network industry, enabling network operators to improve network performance, reduce costs, and deliver better services to their customers.

This collection includes a range of papers that deal with the above issues of networks with emphasis on theory and applications.

Ключевые слова: оптимизация, optimization, принятие решений, Networks, big data, сетевые модели, decision making, большие данные

ПУБЛИКАЦИЯ ПОДГОТОВЛЕНА ПО РЕЗУЛЬТАТАМ ПРОЕКТА: Cетевые модели, оптимизация и вычислительная сложность (2024)