Big data analytics for network and service management

In recent years, we observe a growing trend in the area of big data analytics for network and service management. Approaches such as statistical analysis, data mining, and machine learning have become promising to harness the immense stream of operational data and to improve operations and management of information technology systems and networks. Huge amounts of data from data archives, data centers, cloud systems, Internet of Things, and the Internet are collected, shared, and analyzed for the management of the networks and services. The features of big data, namely, volume, variety, velocity, and veracity, bring new challenges to network and service management. To manage the configuration, performance, resilience, availability, and security of the networks and services, traditional measures such as log/ event analysis, intrusion detection/prevention, and monitoring and deployment have taken a new dimension. New techniques and mechanisms from machine learning, data mining, and data visualization are explored for designing, developing, and operating services and networks in the big data era. In summary, there are a lot of research challenges in this emerging field of data analytics. The purpose of this Special Issue is to explore and highlight the promising capabilities of data analytics in managing the huge streams of operational data on the networks and services. Thirteen papers were submitted for this Special Issue. Four of them are the extended versions of the research presented at the 2016 IEEE/IFIP International Workshop on Analytics for Network and Service Management. After extensive reviews and discussions, six papers were finally selected for publication in this Special Issue. The authors of these papers were given the time to update their papers based on the review comments and suggestions provided. The selected papers address topics that play a central role in using big data analytics for network and service management and presenting novel theoretical and/or experimentation results. The first paper, “Data Transformation as a Means towards Dynamic Data Storage and Polyglot Persistence,” by Vanhove et al is on dynamic storage solutions for big data centers. The authors propose a transformation approach through a canonical model based on the Lambda architecture. The proposed solution is evaluated through a network monitoring platform considered as a use case scenario. The