Ludovic Noirie

Four months in daily motion: Dissecting user video requests

The growth of User-Generated Content (UGC) traffic makes the understanding of its nature a priority for network operators, content providers and equipment suppliers. In this paper, we study a four-month dataset that logs all video requests to DailyMotion made by a fixed subset of users. We were able to infer user sessions from raw data, to propose a Markovian model of these sessions, and to study video popularity and its evolution over time. The presented results are a first step for synthesizing an artificial (but realistic) traffic that could be used in simulations or experimental testbeds.

Software-Defined LANs for Interconnected Smart Environment

In this paper, we propose a solution to delegate the control and the management of the network connecting the many devices of a smart environment to a software entity, while keeping end-users in control of what is happening in their networks. For this, we rely on the logical manipulation of all connected devices through device abstraction and network programmability. Applying Software Defined Networking (SDN) principles, we propose a software-based solution that we call Software-Defined LANs in order to interconnect devices of smart environments according to the services the users are requesting or expecting.We define the adequate virtualization framework based on Virtual Objects and Communities of Virtual Objects. Using these virtual entities, we apply the SDN architectural principles to define a generic architecture that can be applied to any smart environment. Then we describe a prototype implementing these concepts in the home networking context, through a scenario in which users of two different homes can easily interconnect two private but shareable DLNA devices in a dedicated video-delivery SD-LAN. Finally we provide a discussion of the benefits and challenges of our approach regarding the generalization of SDN principles, autonomic features, Internet of Things scalability, security and privacy aspects enabled by SD-LANs intrinsic properties.

A Scalable IoT Service Search Based on Clustering and Aggregation

With the Internet of Things (IoT), we are facing a proliferation of connected devices distributed over a collection of geographical locations and offering new services to users referred to as IoT services. These services are highly heterogeneous and their number is growing fast. To facilitate IoT service search, some solutions rely on the use of Semantic Web technologies to create homogenous service descriptions to reason on and support accurate and flexible search. However, most existing approaches are centralized and fail to scale with respect to the number of advertised services. To address this scalability issue, we present in this paper a distributed, semantic-based IoT service search system. It relies on a hierarchical network of semantic gateways that represent geographical locations and host semantic service descriptions. To answer a service query, our approach is based on request matching and forwarding though the use of routing tables and similarity thresholds. Based on defined metrics, recursive clustering and information aggregation are performed over the hierarchy of gateways to construct the routing tables. We prove theoretically that our method ensures an accurate search of all matching services within the system. Experimental results show that our method enables to reduce greatly search cost with comparison to a centralized approach and has an acceptable search path length.

Evaluation and comparison of MBAC solutions

Admission control is a mechanism used to restrict access to a computer network to some flows based on the current utilization level of the network resource. By regulating the number of on-going flows, admission control aims at preventing overloading, congestion and performance collapses, so that, accepted flows receive a sufficient level of Quality of Service (QoS). In this paper, we evaluate three existing measurement-based admission control (MBAC) solutions, and we compare their efficiency in the context of semantic networks. Semantic networks refer to networks that autonomously acquire a knowledge on the on-going traffic as well as on any new incoming flow requesting admission. In this framework, we configure the three MBAC solutions in a way they have an identical target in terms of maximum tolerable packet loss rate or maximum tolerable packet queueing delay. We evaluate the solutions performance analytically or by simulation, and compare them to the “ideal” admission control. The results show that one solution, outperforms the others in meeting the target performance.1

KBAC: Knowledge-Based Admission Control

Many methods have been proposed in the literature to perform admission control in order to provide a sufficient level of Quality of Service (QoS) to accepted flows. In this paper, we introduce a novel data-driven method based on a time-varying model that we refer to as Knowledge-Based Admission Control solution (KBAC). Our KBAC solution consists of three main stages: (i) collect measurements on the on-going traffic over the communication link; (ii) maintain an up-to-date broad view of the link behavior, and feed it to a Knowledge Plane; (iii) model the observed link behavior by a mono-server queue whose parameters are set automatically and which predicts the expected QoS if a flow requesting admission were to be accepted. Our KBAC solution provides a probabilistic guarantee whose admission threshold is either expressed, as a bounded delay or as a bounded loss rate. We run extensive simulations to assess the behavior of our KBAC solution in the case of a delay threshold. The results show that our KBAC solution leads to a good trade-off between flow performance and resource utilization. This ability stems from the quick and automatic adjustment of its admission policy according to the actual variations on the traffic conditions.

Efficient semantic-based IoT service discovery mechanism for dynamic environments

The adoption of Service Oriented Architecture (SOA) and semantic Web technologies in the Internet of Things (IoT) enables to enhance the interoperability of devices by abstracting their capabilities as services and to enrich their descriptions with machine-interpretable semantics. This facilitates the discovery and composition of IoT services. The increasing number of IoT services, their dynamicity and geographical distribution require mechanisms to enable scalable and efficient discovery. We propose in this paper a semantic based IoT service discovery system that supports and adapts to the dynamicity of IoT services. The discovery is distributed over a hierarchy of semantic gateways. Within a semantic gateway, we implement mechanisms to dynamically organize its content over time, in order to minimize the discovery cost. Results show that our approach enables to maintain a scalable and efficient discovery and limits the number of updates sent to a neighboring gateway.

Networks for high-bandwith services combining photonic circuit cross-connects with packet switches

We describe the dimensioning and benchmarking of packet transport networks with aggregated link capacities up to the Tbit/s range, using a circuit-switching (cross-connect) infrastructure below packet-switching nodes. The different network solutions to be evaluated are designed to carry large numbers of high-definition video and other data streams, with upper bounds on packet latency and packet loss rate for all flows. Trends of evolution are analyzed, based on a performance and cost comparison of several node architectures with different associated transport technologies (packet and circuit layers). Simulations of packet-switch nodes under pseudo-self-similar traffic, extraction of analytical models for latency and loss, end-to-end best path selection, and constrained optimization of crossconnect and packet-switch resources are combined to derive the dimensioning of all the network elements. As a result of this study, photonic cross-connects associated with carrier-grade Ethernet switches appear as the most efficient architectures, thanks to cost savings for transit traffic. Single-layer solutions involving for example only packet-switch routers turn out to be more expensive.

Physical-Interface-Based IoT Service Characterization

Connected devices -- as key constituent elements of the Internet of Things (IoT) -- are exponentially flooding our real world environment, making it smarter (smart home / building / city / etc). This unprecedented digital wave paves the way for a major technological breakthrough called to deeply change our daily lives. Nevertheless, in order to make this announced revolution come true, some strong issues remain to be addressed. The most dominant one relates to our ability to leverage the whole IoT service space and more specifically to our ability to compose IoT services from multiple connected devices by cleverly selecting them with the required software functions whatever our technical skills. In such a challenging context, this paper presents a model-based approach particularly allowing an autonomous recommendation of available IoT services to end-users. To support this vision, a rich and flexible abstraction framework relying on Attributed Typed Graphs has been used. The latter formalism enables to represent how known IoT services are composed from different perspectives. Capitalizing on this modeling tool and first focusing on the way IoT services interact with the physical environment, lightweight service signatures are computed by using an innovative physical-interfaced-based algorithm. Finally, we discuss how leveraging the computed signatures can allow for autonomously recommending viable IoT services according to available connected devices.

Towards automated IoT service recommendation

Today, people buy connected objects for limited usages, although these objects could be used in many other IoT services they are not aware of. In this demo paper, we illustrate a solution that helps people to better leverage their connected objects by recommending all services they could benefit from.

On the manipulability of voting systems: application to multi-operator networks

Internet is a large-scale and highly competitive economic ecosystem. In order to make fair decisions, while preventing the economic actors from manipulating the natural outcome of the decision process, game theory is a natural framework, and voting systems represent an interesting alternative that, to our knowledge, has not yet being considered. They allow competing entities to decide among different options. In this paper, we investigate their use for end-to-end path selection in multi-operator networks, analyzing their manipulability by tactical voting and their economic efficiency.We show that Instant Runoff Voting is much more efficient and resistant to tactical voting than the natural system which tries to get the economic optimum.