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Special Issue on Advances in Big Data Analytics for Management

Cloud and network analytics can harness the immense stream of operational data from clouds and networks, and can perform analytics processing to improve reliability, configuration, performance, and security management. In particular, we see a growing trend towards using statistical analysis and machine learning to improve operations and management of IT systems and networks.

Research is therefore needed to understand and improve the potential and suitability of Big Data analytics in the context of systems and network management. This will not only provide deeper understanding and better decision making based on largely collected and available operational data, but present opportunities for improving data analysis algorithms and methods on aspects such as accuracy and scalability. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advanced data analysis algorithms to drive management decisions in networks, data centers, and clouds.

IEEE Transactions on Network and Service Management (IEEE TNSM) is a premier journal for timely publication of archival research on the management of networks, systems, services and applications. Following the success of TNSM special issue on Big Data Analytics for Management (September 2016), this special issue of TNSM will focus on “Advances in Big Data Analytics for Management,” presenting recent, emerging approaches and technical solutions that can exploit Big Data and analytics in management solutions. We welcome submissions addressing the underlying challenges of Big Data Analytics for Management and presenting novel theoretical or experimentation results. Survey papers that offer a perspective on related work and identify key challenges for future research will be considered as well.

Topics of Interest

Topics of interest for this special issue include, but are not limited, to the following:

  • Big Data Analytics and Machine Learning
    • Analysis, modelling and visualization
    • Operational analytics and intelligence
    • Event and log analytics
    • Anomaly detection and prediction
    • Monitoring and measurements for management
    • Harnessing social data for management
    • Predictive analytics and real-time analytics
    • Data mining, statistical modeling, and machine learning for management
  • Application Domains and Management Paradigms
    • Cloud and network analytics
    • Data centric management of virtualized infrastructure, clouds and data centers
    • Data centric management of storage resources and software defined networks
    • Data centric management of Internet of Things and cyber-physical systems
    • Platforms for analyzing and storing logs and operational data for management tasks
    • Applications of Big Data analytics to traffic classification, root-cause analysis, service quality assurance, IT service and resource management
    • Analytics and machine learning applications to cyber-security, intrusion detection, threat analysis, and failure detection


Giuliano Casale
Imperial College London, UK

Yixin Diao
IBM T. J. Watson Research Center, USA

Marco Mellia
Politecnico di Torino, Italy

Rajiv Ranjan
Newcastle University, UK

Nur Zincir-Heywood
Dalhousie University, Canada

For more information, please contact the guest editors at

(* online published version will be available in IEEE Xplore after the camera ready version has been submitted with final doi)