iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.Comment: 12 pages, 16 figure

Big data reduction framework for value creation in sustainable enterprises

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network