[DMANET] CFP - IEEE TNSM Special Issue on Data Analytics and Machine Learning for Network and Service Management

IEEE Transactions on Network and Service Management (impact factor: 4.682)

Special Issue on Data Analytics and Machine Learning for Network and
Service Management

https://www.comsoc.org/publications/journals/ieee-tnsm/cfp/data-analytics-and-machine-learning-network-and-service

(Submissions due: 2 April 2020)

Network and Service analytics can harness the immense stream of operational
data from clouds, to services, to social and communication networks. In the
era of big data and connected devices of all varieties, analytics and
machine learning have found ways to improve reliability, configuration,
performance, fault and security management. In particular, we see a growing
trend towards using machine learning, artificial intelligence and data
analytics to improve operations and management of information technology
services, systems and networks.
Research is therefore needed to understand and improve the potential and
suitability of data analytics and machine learning in the context of
services, systems and network management. This will provide deeper
understanding and better decision making based on largely collected and
available operational and service data. It will also present opportunities
for improving machine learning and data analytics algorithms and methods on
aspects such as reliability, dependability and scalability, as well as
demonstrate the benefits of these methods in management and control
systems. 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 three recent TNSM special issues on Big Data Analytics for
Management in 2016, 2018, and 2019, this special issue will also focus on
recent, emerging approaches and technical models that exploit data
analytics and machine learning in network and service management solutions.
We welcome submissions addressing the underlying challenges and
opportunities, presenting novel techniques, experimental results, or
theoretical approaches motivated by management problems. Survey papers that
offer a perspective on related work and identify key challenges and
opportunities for future research are also in the scope of the special
issue. We look forward to your submissions!

Topics of Interest:

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

Data Analytics, Machine Learning and Artificial Intelligence

Analysis, modelling and visualization
Operational analytics and intelligence
Event and log analytics, text mining
Outlier / Anomaly detection and prediction
Monitoring and measurements for management
Predictive analytics and real-time analytics
Artificial intelligence, neural networks, and deep learning for management
Data mining, statistical modeling, and machine learning for management

Application Domains and Management Paradigms

Cloud and network analytics
Social and communication networks analysis
Data centric management of virtualized infrastructure, clouds and data
centers
Data centric management of software defined networks
Data centric management of storage resources
Data centric management of Internet of Things and cyber-physical systems
Data centric management of zero touch and driverless networks
Platforms for analyzing and storing logs and operational data for
management tasks
Applications of data analytics to traffic classification, root-cause
analysis, service quality assurance, IT service and resource management
Novel approaches to cyber-security, intrusion detection, threat analysis,
and failure detection based on data analytics and machine learning


Submission Guidelines:

All papers should be submitted through the IEEE Transactions on Network and
Service Management manuscript submission site. Authors must indicate in the
submission cover letter that their manuscript is intended for the "Data
Analytics and Machine Learning for Network and Service Management" special
issue. View detailed author guidelines.

Important Dates:

Paper Submission: 2 April 2020
Review Results Returned: 15 June 2020
Revision Submission: 15 July 2020
Final Acceptance Notification: 15 September 2020
Final Paper Submission: 7 October 2020
Publication Date (Tentative): December 2020

Guest Editors:

Nur Zincir-Heywood (Dalhousie University, Canada)
Giuliano Casale (Imperial College London, UK)
David Carrera (Barcelona Supercomputing Center, Spain)
Amogh Dhamdhere (Amazon Web Services, USA)
Takeru Inoue (NTT Laboratories, Japan)
Hanan Lutfiyya (The University of Western Ontario, Canada)
Taghrid Samak (Google, USA)

For more information, please contact the guest editors at
TNSM.SI.DAML20@gmail.com.

**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************