Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe

Peer-to-peer overlay networks are attractive solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a message published on a certain topic often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its subscribers. This might sharply increase resource consumption for such relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. In this paper, we introduce Vitis, a gossip-based publish/subscribe system that significantly decreases the number of relay messages, and scales to an unbounded number of nodes and topics. This is achieved by the novel approach of enabling rendezvous routing on unstructured overlays. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network, which spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. We perform extensive simulations and evaluate Vitis by comparing its performance against two base-line publish/subscribe systems: one that is oblivious to node subscriptions, and another that exploits the subscription similarities. Our measurements show that Vitis significantly outperforms the base-line solutions on various subscription and churn scenarios, from both synthetic models and real-world traces

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

ICONA: Inter Cluster ONOS Network Application

Several Network Operating Systems (NOS) have been proposed in the last few years for Software Defined Networks; however, a few of them are currently offering the resiliency, scalability and high availability required for production environments. Open Networking Operating System (ONOS) is an open source NOS, designed to be reliable and to scale up to thousands of managed devices. It supports multiple concurrent instances (a cluster of controllers) with distributed data stores. A tight requirement of ONOS is that all instances must be close enough to have negligible communication delays, which means they are typically installed within a single datacenter or a LAN network. However in certain wide area network scenarios, this constraint may limit the speed of responsiveness of the controller toward network events like failures or congested links, an important requirement from the point of view of a Service Provider. This paper presents ICONA, a tool developed on top of ONOS and designed in order to extend ONOS capability in network scenarios where there are stringent requirements in term of control plane responsiveness. In particular the paper describes the architecture behind ICONA and provides some initial evaluation obtained on a preliminary version of the tool.Comment: Paper submitted to a conferenc

Project Testbed: Argument Mapping and Deliberation Analytics

One key goal of the Catalyst project was to design metrics that could capture and represent aspects of the conversation’s structural quality, to assist harvesters and moderators. Many such metrics, alerts and visualizations were developed in the course of the project, but initial user testing has shown that users find it difficult to interpret abstract signals. Following that, we have both introduced new analytics that we felt could be more directly useful, and improved the representation of existing ones. We evaluated their usefulness in a smaller conversation and in experimental settings

MAP: Microblogging Assisted Profiling of TV Shows

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relationship of its microblogging users and its propagation in the social network; iii) We present some interesting analysis, based on our framework to profile real-world TV shows