Title: Big Data, Machine Learning, Data Science for Traffic Monitoring and Analysis
Abstract: Network Traffic Monitoring and Analysis represents a key component for network management to guarantee the correct operation of large-scale networks such as the Internet. As the complexity of Internet services and the volume of traffic continue to increase, it becomes difficult to design scalable and powerful solutions to support the monitoring of the liveness of networks. Applications such as traffic classification and policing require real-time and scalable approaches. Anomaly detection and security mechanisms require to quickly identify and react to unpredictable events while processing millions of heterogeneous events. At last, the system has to collect, store, and process massive sets of historical data for post-mortem analysis. Those are precisely the challenges faced by general Big Data approaches: Volume, Velocity, Variety, and Veracity. Thus Network Traffic Monitoring and Analysis qualifies for adopting big data approaches to understand what is going on in the Internet. In this talk, I’ll mainly focus on approaches and technologies to face the network monitoring and analysis process, discussing the adoption of Machine Learning based analytics and Data Science approaches specifically designed to answer both simple and complex questions Internet providers face everyday. I’ll present the lessons learned after more then 20 year of study in this area, and challenges that are still open.
Speaker: Marco Mellia, Politecnico di Torino (Italy)
Bio: Marco Mellia is Full Professor in the Electronics and Telecommunications Department at Politecnico di Torino. After spending 1 year in Carnegie Mellon University, he visited the Sprint Advanced Technology Laboratories, Cisco Systems, Narus Inc in the Bay Area. He collaborated with several key players in the industry and coordinated European Projects, in the area of traffic monitoring and cyber-security system design. His research interests are now in traffic monitoring and analysis, and in applications of Big Data and Machine Learning techniques for traffic analysis, Cybersecurity and network monitoring. He has co-authored over 250 papers, holds 10 patents, and co-funded Ermes Cyber Security, a startup providing Machine Learning solutions for online privacy protection. He was awarded the IRTF Applied Networking Research Prize, and obtained several best paper awards. He is Area Editor of ACM CCR, part of the Editorial Board of IEEE/ACM Transactions on Network and Service Management and Elsevier Computer Networks.
He is now the director of the SmartData@PoliTO center which aggregates more than 50 experts in Big Data and Machine Learning investigating Data Science approaches in different areas.
Title: How future networks will become proactive? - Machine learning to solve a pertinent engineering challenge.
Abstract: The cellular network have long been configured and optimised reactively by identifying events and triggers and readjusting the operation of the cellular system. Our research is paving the way to make a step change by introducing proactive techniques to pre-emptively trigger actions that will make the networks more agile in adapting to changing demands of service and quality of experience. With the advent of ultra-dense deployment of networks, we need to use such mechanisms to schedule multi-level sleep modes of cells, mobility management as well as joint RAN-backhaul optimisation from efficiency perspective. As a use case, we will focus on the energy efficiency aspect covering the fundamental framework for the evaluation of energy efficiency and the state of the art as well as futuristic approaches to achieve energy efficiency.
Speaker: Muhammad Ali Imran, University of Glasgow (UK)
Bio: Prof. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is a Professor of Communication Systems in University of Glasgow, Vice Dean of Glasgow College UESTC. He is an Affiliate Professor at the University of Oklahoma, USA and a visiting Professor at the 5G Innovation Centre at the University of Surrey, UK. He has led a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He also led the physical layer work area for 5G innovation centre and was the faculty lead for “Engineering for Health” programme at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 40+ successful PhD graduates and published over 400 peer-reviewed research papers including more than 100 IEEE Journal papers. He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. In addition to 8 Best Conference Paper Awards in international conferences, he has been awarded IEEE Comsoc’s Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean’s Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014, Reimagine Education Awards for innovative teaching and VC’s learning and teaching award in the University of Surrey. He is a fellow of IET, a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK.
Title: Networks of the future are driverless.
Abstract: Automated management has been the holy grail of network management research for decades; it aims at achieving autonomous networks, i.e., networks capable to autonomously monitor their status, analyze problems, make decisions, and execute corrective actions. Despite several attempts to achieve autonomous networks in the past, their practical deployments have largely remained unrealized. Several factors are attributed to this, including the existence of many stakeholders with conflicting goals, reliance on proprietary solutions, the inability to process network monitoring data at scale, and the lack of global visibility restricting network-wide optimizations. The stars are now aligned to realize the vision of autonomous networks thanks to (i) advances in network softwarization; (ii) recent breakthroughs in machine learning; and (iii) the availability of large-scale data processing platforms. However, a number of challenges must be addressed in order to create the synergy between these different technology domains and achieve autonomous (a.k.a., driverless networks) networks. This talk will discuss some of these challenges with particular focus on programmable network monitoring leveraging network softwarization, predictive machine learning for automated management decision making, and on-demand orchestration of network services.
Speaker: Raouf Boutaba, University of Waterloo (Canada)
Bio: Raouf Boutaba is a University Chair Professor of Computer Science and Associate Dean Research of the faculty of Mathematics at the University of Waterloo. He also holds an INRIA International Chair in France. He is the founding Editor in Chief of the IEEE Transactions on Network and Service Management (2007-2010), and the current Editor-in-Chief of the IEEE Journal on Selected Areas in Communications. He served as the general or technical program chair for a number of international conferences including IM, NOMS and CNSM. His research interests are in the areas of network and service management. He has published extensively in these areas and received several journal and conference Best Paper Awards such as the IEEE 2008 Fred W. Ellersick Prize Paper Award. He also received other recognitions, including the Premier's Research Excellence Award, Industry research excellence Awards, fellowships of the Faculty of Mathematics, of the David R. Cheriton School of Computer Science and several outstanding performance awards at the University of Waterloo. He has also received the IEEE Communications Society Hal Sobol Award and the IFIP Silver Core in 2007, the IEEE Communications Society Joe LociCero and the Dan Stokesbury awards in 2009, the Salah Aidarous award in 2012, the IEEE Canada McNaugthon Gold Medal in 2014, the Technical Achievement Award of the IEEE Technical Committee on Information Infrastructure and Networking as well as the Donald W. McLellan Meritorious Service Award in 2016. He served as a distinguished lecturer for the IEEE Computer and Communications Societies. He is fellow of the IEEE, a fellow of the Engineering Institute of Canada and a fellow of the Canadian Academy of Engineering.