Topology of communities for the collaborative recommendations to groups

International audienceMore and more systems allow user personalization and provide item recommendations, intended to fit individual user interests. In a traditional VoD system, for example, the recommendations are oriented towards a single user even though he is not watching the video alone. Hence, there is a need to have recommendations for a set of users, a group. Collaborative filtering techniques are traditionally used to make a recommendation for a single user. Usage traces or user ratings are used to deduce their profile and to select an appropriate recommendation that way. Performing recommendation for groups is considerably more difficult because the retrieval of a group's traces of usage or ratings is complicated. As the individual profile for each member of the group is usually available, the recommendation for a group can be based on these individual profiles. This paper explores this approach and is the first step of the construction of a software toolkit for computing recommendations in function of the group composition and the chosen strategies

Machine Learning Approaches to Early Fault Detection and Identification in NFV Architectures

International audienceVirtualization technologies become pervasive in networking, as a way to better exploit hardware capabilities and to quickly deploy tailored networking solutions for customers. But these new programmability abilities of networks also come with new management challenges: it is critical to quickly detect performance degradation, before they impact Quality of Service (QoS) or produce outages and alarms, as this takes part in the closed loop that adapts resources to services. This paper addresses the early detection, localization and identification of faults, before alarms are produced. We rely on the abundance of metrics available on virtualized networks, and explore various data preprocessing and classification techniques. As all Machine Learning approaches must be fed with large datasets, we turn to our advantage the softwarization of networks: one can easily deploy in a cloud the very same software that is used in production, and analyze its behaviour under stress, by fault injection

X-domain QoS budget negotiation using Dynamic Programming

Quality of Service (QoS) has been a major concern in the field of network management, even more so for emerging dynamic multimedia applications (Video on Demand, Telefony over IP etc.) that are becoming mainstream. This problem is particularly sensitive in the context of exchanges accross multiple independent and heterogeneous domains (X-domain), where global SLAs (Service Level Agreement) have to be satisfied accross domains. In this paper, we consider a typical scenario of X-domain provisioning of a video-conference session. The article addresses the problem of how to automatically negotiate QoS budgets between possible service providers (SP) that will meet the end-to-end requirements. We propose a QoS budget negotiation algorithm based on the dynamic programming principle. The negotiation is strictly distributed in the sense that all contracts are agreed on bilaterally between adjacent SPs, and each SP becomes responsible for the subbudget between it and the end-domain

Algorithms for Distributed Fault Management in Telecommunications Networks

International audienceDistributed architectures for network management have been the subject of a large research effort, but distributed algorithms that implement the corresponding functions have been much less investigated. In this paper we describe novel algorithms for model-based distributed fault diagnosis

User and Usage Profiling in a Multi-Platform Service Environment

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FORPS: Friends Oriented Reputation Privacy Score

International audienceThe Friends-Oriented Reputation Privacy Score (FORPS) system provides a smart and simple way to help end-users managing their privacy in a social network. It aims to prevent a non-desirable propagation of personal sensitive data. FORPS built privacy sensitivity profile by understanding what are the category of themes, the category of objects and the behavioral factors that are important to social network users. FORPS takes full advantage of the knowledge available in a social network from the perspective of a given user, in particular extracted from the data accessible via his friends. More precisely, our approach consists in making a deep analysis of the behavior of somebody who would like to establish connection with the given user in order to estimate the risk of potential violation of his privacy