Please consider submitting your work to IEEE TNSM - deadline Mar 31st
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Farhana Zulkernine
Mar 3, 2022, 8:33:53 PM3/3/22
to Machine Learning News
IEEE Transactions on Network and Service Management (impact factor: 4.195)
Special Issue on Machine Learning and Artificial Intelligence for Managing Networks, Systems and Services
https://www.comsoc.org/publications/journals/ieee-tnsm/cfp/machine-learning-and-artificial-intelligence-managing-networks
(Submissions due: 31 March 2022)
Call for Papers
Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of big data and connected devices of all varieties, ML/AI have found ways to improve operations and
management of information technology networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, systems, and service management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.
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, and services. Following the success of five recent TNSM special issues on Data Analytics for Network and Service Management in 2016, 2018, 2019, 2020 and 2021, this special issue will also focus on recent, emerging approaches and technical models that exploits ML/AI 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 opportunities and challenges 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:
ML/AI Techniques for Network and Service Management
- Analysis, modelling and visualization
- Operational analytics and intelligence
- Event and log analytics, text mining
- Outlier / Anomaly detection and prediction
- Predictive analytics and real-time analytics
- ML/AI, neural networks, and deep learning for management
- Data mining, statistical modeling, and machine learning for management
Application Domains and Management Paradigms:
- Social and communication networks analysis
- ML/AI management of virtualized infrastructure, clouds, and edge nodes
- ML/AI management of software defined networks
- ML/AI management of storage resources
- ML/AI management of Internet of Things and cyber-physical systems
- ML/AI management of 5G/6G
- ML/AI management of zero touch and driverless networks
- Applications of AI 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 ML/AI
- Platforms for monitoring and measurements to support management for ML/AI
- Platforms for analyzing and storing logs and operational data for management tasks
- Platforms for collaborative learning from multiple distributed network elements
- Platforms for anonymizing operation data
- AI/ML testbeds and experimental evaluations
- Abstractions and knowledge representation/data models needed for deploying ML/AI for network management and orchestration
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 "Machine Learning and Artificial Intelligence for Managing Networks, Systems and Services” special issue. View detailed author guidelines.
Important Dates:
Paper Submission: 31 March 2022
Publication Date: December 2022
Guest Editors:
Nur Zincir-Heywood (Lead), Dalhousie University, Canada
Robert Birke, ABB Research, Switzerland
Giuliano Casale, Imperial College London, UK
Elias Bou-Harb, The University of Texas at San Antonio, USA
Takeru Inoue, NTT Laboratories, Japan
Neeraj Kumar, CSED Thapar Institute of Engineering and Technology, India
Hanan Lutfiyya, The University of Western Ontario, Canada
Deepak Puthal, Newcastle University, UK
Farhana Zulkernine, Queen’s University, Canada
For more information, please see:
https://www.comsoc.org/publications/journals/ieee-tnsm/cfp/machine-learning-and-artificial-intelligence-managing-networks
Special Issue on Machine Learning and Artificial Intelligence for Managing Networks, Systems and Services
https://www.comsoc.org/publications/journals/ieee-tnsm/cfp/machine-learning-and-artificial-intelligence-managing-networks
(Submissions due: 31 March 2022)
Call for Papers
Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of big data and connected devices of all varieties, ML/AI have found ways to improve operations and
management of information technology networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, systems, and service management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.
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, and services. Following the success of five recent TNSM special issues on Data Analytics for Network and Service Management in 2016, 2018, 2019, 2020 and 2021, this special issue will also focus on recent, emerging approaches and technical models that exploits ML/AI 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 opportunities and challenges 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:
ML/AI Techniques for Network and Service Management
- Analysis, modelling and visualization
- Operational analytics and intelligence
- Event and log analytics, text mining
- Outlier / Anomaly detection and prediction
- Predictive analytics and real-time analytics
- ML/AI, neural networks, and deep learning for management
- Data mining, statistical modeling, and machine learning for management
Application Domains and Management Paradigms:
- Social and communication networks analysis
- ML/AI management of virtualized infrastructure, clouds, and edge nodes
- ML/AI management of software defined networks
- ML/AI management of storage resources
- ML/AI management of Internet of Things and cyber-physical systems
- ML/AI management of 5G/6G
- ML/AI management of zero touch and driverless networks
- Applications of AI 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 ML/AI
- Platforms for monitoring and measurements to support management for ML/AI
- Platforms for analyzing and storing logs and operational data for management tasks
- Platforms for collaborative learning from multiple distributed network elements
- Platforms for anonymizing operation data
- AI/ML testbeds and experimental evaluations
- Abstractions and knowledge representation/data models needed for deploying ML/AI for network management and orchestration
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 "Machine Learning and Artificial Intelligence for Managing Networks, Systems and Services” special issue. View detailed author guidelines.
Important Dates:
Paper Submission: 31 March 2022
Publication Date: December 2022
Guest Editors:
Nur Zincir-Heywood (Lead), Dalhousie University, Canada
Robert Birke, ABB Research, Switzerland
Giuliano Casale, Imperial College London, UK
Elias Bou-Harb, The University of Texas at San Antonio, USA
Takeru Inoue, NTT Laboratories, Japan
Neeraj Kumar, CSED Thapar Institute of Engineering and Technology, India
Hanan Lutfiyya, The University of Western Ontario, Canada
Deepak Puthal, Newcastle University, UK
Farhana Zulkernine, Queen’s University, Canada
For more information, please see:
https://www.comsoc.org/publications/journals/ieee-tnsm/cfp/machine-learning-and-artificial-intelligence-managing-networks
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