AI and Machine Learning are currently being exploited in almost every scientific fields. However, networking has still a limited development and deployment of these techniques.
AI can be effectively used in many networking areas, such as fault isolation, intrusion detection, event correlation, log analysis, capacity planning, and design optimization, just to name a few. Moreover, the complexity of today networks makes it challenging to design scalable network measurement and analysis techniques and tools. Machine learning and big data analytics techniques promise to shed light on this enormous amount of data, but smart and scalable approaches must be conceived to make them applicable to the networking practice.
WAIN workshop aims at showing to the community new contributions in this field. It looks for smart approaches and uses cases useful for understanding when and how applying AI in networking. The workshop will allow researchers and practitioners to share their experiences and ideas and discuss the open issues related to the application of machine learning to computer networks data.
This workshop will include both contributed and invited papers.
Papers will be published at ACM SIGMETRICS Performance Evaluation Review (https://www.sigmetrics.org/per.shtml,*2 to 4 page long*). Furthermore, authors will have the option to submit a longer version of their paper to one of the special issues.
Topics of Interest
The following is a non-exhaustive list of topics of interest for WAIN workshop:
- Applications of ML in communication networks
- Data analytics and mining in networking
- Traffic monitoring through AI
- Application of deep learning and reinforced-learning in networking
- Benchmarks design for big data or ML
- Protocol design using ML
- Methodologies for network anomaly detection and cybersecurity
- Visualization for network characteristics and trafficmonitoring
- Requirements and expectations when using AI
- AI applied to IoT, 5G or cloud
- Performance Optimization through ML and Big Data
- Experiences and best-practices using machine learning in operational networks
- Reproducibility of ML in networking
- Paper submissions deadline: *September 12th, 2018*, 11:59:59 EDT (midnight)
- Notification of Acceptance: *October 12th, 2018*
Submissions must be original, unpublished work, and not under consideration at another conference or journal. The format for the submissions is that of PER (two-column 10pt ACM format), *between 2 and 4 pages long,* including all figures, tables, references, and appendices. Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Authors of accepted papers are expected to present their papers at the workshop.
EasyChair submission website will be online within few days.
- Luca Vassio <https://www.tlc-networks.polito.it/public/phd-and-post-docs/luca-vassio>, Politecnico di Torino, Italy
- Zhi-Li Zhang <http://www-users.cs.umn.edu/~zhang089/>, University of Minnesota, US
- Sung-Ju Lee <https://sites.google.com/site/wewantsj/home>, KAIST, Korea
- Marco Canini, KAUST (Saudi Arabia)
- Song Chong, KAIST, (South Korea)
- Edmundo de Souza e Silva, Federal University of Rio de Janeiro (Brazil)
- Lixin Gao, UMASS (USA)
- Danilo Giordano, Politecnico di Torino (Italy)
- Leana Golubchik, University of Southern California (USA)
- Dan Li, Tsinghua University (China)
- Marco Mellia, Politecnico di Torino (Italy)
- Giovanni Neglia, Inria (France)
- Rayadurgam Srikant, UIUC (USA)
- Patrick Thiran, EPFL (Switzerland)
- Martino Trevisan, Politecnico di Torino (Italy)
- Hui Zang, Huawei Santa Clara (USA)
- Nur Zincir-Heywood, Dalhousie University (Canada)
(Submissions due: 15 November 2018)
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, fault and security management. In particular, we see a growing trend towards using statistical analysis, Artificial Intelligence (AI) 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 and AI 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, as well as demonstrate the benefits of machine intelligence methods in system and network management and control. 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 two recent TNSM special issues on Big Data Analytics for Management in 2016 and 2018, this special issue will also focus on recent, emerging approaches and technical solutions that can exploit Big Data, analytics, and AI in management solutions. We welcome submissions addressing the underlying challenges of Big Data Analytics for Management and 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 for future research are also in the scope of the special issue.
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, text mining
- Anomaly detection and prediction
- Monitoring and measurements for management
- Harnessing social data 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
- 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
- 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
- Novel approaches to cyber-security, intrusion detection, threat analysis, and failure detection based on Big data analytics and machine learning
All papers should be submitted through the IEEE Transactions on Network and Service Management manuscript submission site at https://mc.manuscriptcentral.com/tnsm. Authors must indicate in the submission cover letter that their manuscript is intended for the “Novel Techniques in Big Data Analytics for Management” special issue. Each submission will be limited to 14 pages in IEEE 2-column format. Detailed author guidelines can be found at http://www.comsoc.org/tnsm/author-guidelines.
- Paper submission: November 15, 2018
- Review results returned: February 15, 2019
- Revision submission: March 15, 2019
- Final acceptance notification: June 15, 2019
- Final paper submission: July 7, 2019
- Publication date (tentative): September 2019*
(* online published version will be available in IEEE Xplore after the camera ready version has been submitted with final DOI)
- David Carrera (Barcelona Supercomputing Center, Spain)
- Giuliano Casale (Imperial College London, UK)
- Takeru Inoue (NTT Laboratories, Japan)
- Hanan Lutfiyya (The University of Western Ontario, Canada)
- Jia Wang (AT&T Research, US)
- Nur Zincir-Heywood (Dalhousie University, Canada)
For more information, please contact the guest editors at TNSM.SI.BDM19@gmail.com
Series on Data Science and Artificial Intelligence for Communications
The objective of the Data Science and Artificial Intelligence for Communications Series of the IEEE Communications Magazine is to provide a forum across industry and academia to advance the development of network and system solutions using data science and artificial intelligence.
Innovations in artificial intelligence, machine learning, reinforcement learning and network data analytics introduce new opportunities in various areas, such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi-agent systems, prioritization of network ultra-broadband deployments. These new analytic platforms will help revolutionize our networks and user experience. Through gathering, processing, learning and controlling the vast amounts of information in an intelligent manner future networks will enable unprecedented automation and optimization.
This Series solicits articles addressing numerous topics within its scope including, but not limited to, the following:
- All aspects of artificial intelligence, machine learning, reinforcement learning and data analytics aiming at enabling and enhancing next generation networks. The scope of issues that can be addressed includes both conventional measures such as traffic management, QoE, service quality, as well as future network behavior through intelligent services and applications.
- Methods, systems and infrastructure for the analysis of network, service traffic and user behavior for efficient and reliable design of networks, including deep learning and statistical methods for network tomography.
- Predictive analytics and artificial intelligence for network optimization, network security, network assurance, and data privacy and integrity. Diagnosis of network failures using analytics and AI.
- Automated communication infrastructure among smart machines and agents (including humans, e.g. speech and vision), and information fusion for automation and enablement of multi-agent systems.
- Communication and networking to facilitate smart data-centric applications
Manuscripts must be submitted through the magazine’s submissions Website at http://mc.manuscriptcentral.com/commag-ieee. You will need to register and then proceed to the author center. On the manuscript details page, please select Data Science and Artificial Intelligence for Communications Series from the drop-down menu. Manuscripts should be tutorial in nature and should not be under review for any other conference or journal. They should be written in a style comprehensible and accessible to readers outside the specialty of the article. Mathematical equations should not be used. For detailed submission guidelines please refer to the magazine website for the list of guidelines that must be followed by all submissions to the IEEE Communications Magazine: https://www.comsoc.org/commag/paper-submission-guidelines
Authors are encouraged to contact the Series Editor before submitting an article in order to ensure that the article will be appropriate for the Series. Papers can be submitted anytime during the year. They will receive a review process, and, if accepted, they will be published in the first slot available for this Series.
- Irena Atov, Microsoft, USA (email@example.com)
- Kwang-Cheng Chen, University of South Florida, USA (firstname.lastname@example.org)
- Shui Yu, University of Technology Sydney, Australia (email@example.com)