Many choice problems cannot be solved exactly and use several estimation algorithms that assign scores to the different available options. The estimation errors can have various correlations, from low (e.g. between two very different approaches) to high (e.g. when using a given algorithm with different hyperparameters). Most aggregation rules would suffer from this diversity of correlations. In this article, we introduce Embedded Voting (EV), an aggregation rule designed to take correlations into account, and we compare it to other aggregation rules in various experiments based on synthetic data. Our results show that when sufficient information about the correlations between errors is available, a maximum likelihood aggregation should be preferred. Otherwise, typically with limited training data, EV outperforms the other approaches.

The evolution of LTE and advent of 5G networks increases further the bandwidth requirements for RAN. In parallel, the deployment of Centralized RAN architecture raises new challenges on the FrontHaul network. The inflexibility of the legacy CPRI is the primary challenge to Virtualized RAN deployments, and there is currently a strong trend towards the use of packetized transport methods, together with flexible split RAN based architectures. Functional splits within the real-time functions of the RAN have very stringent requirements on latency and jitter. This paper analyzes the jitter produced in the switching nodes of the FrontHaul network, and proposes dimensioning rules.

We present SVVAMP, a Python package dedicated to the study of voting systems with an emphasis on manipulation analysis.

We investigate the possibility of designing a voting rule that both meets the Condorcet criterion and has a low vulnerability to coalitional manipulation. For this purpose, we examine the Condorcification of a voting rule, where the original rule is altered to elect the Condorcet winner when one exists, and we study its impact on coalitional manipulability. A recent result states that for a large class of voting rules based on strict total orders, their Condorcification is at most as coalitionally manipulable as the original rule. We show that for most of them, the improvement is strict. We extend these results to a broader framework that includes weak orders and cardinal voting rules. These results support the main message of this paper: when searching for a “reasonable” voting rule with minimal coalitional manipulability, investigations can be restricted to Condorcet rules. In other words, in a class of “reasonnable” voting rules, it is possible to have both the Condorcet criterion and a minimal vulnerability to coalitional manipulation.

One of the key features of small-worlds is the ability to route messages with few hops only using local knowledge of the topology. In 2000, Kleinberg proposed a model based on an augmented grid that asymptotically exhibits such property. In this paper, we propose to revisit the original model from a simulation-based perspective. Our approach is fueled by a new algorithm that uses dynamic rejection sampling to draw augmenting links. The speed gain offered by the algorithm enables a detailed numerical evaluation. We show for example that in practice, the augmented scheme proposed by Kleinberg is more robust than predicted by the asymptotic behavior, even for very large finite grids. We also propose tighter bounds on the performance of Kleinberg's routing algorithm. At last, we show that fed with realistic parameters, the model gives results in line with real-life experiments.

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.

We propose a new model for peer-to-peer networking which takes the network bottlenecks into account beyond the access. This model can cope with key features of P2P networking like degree or locality constraints together with the fact that distant peers often have a smaller rate than nearby peers. Using a network model based on rate functions, we give a closed form expression of peers download performance in the system's fluid limit, as well as approximations for the other cases. Our results show the existence of realistic settings for which the average download time is a decreasing function of the load, a phenomenon that we call super-scalability.

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.

Seeders (peers that do not request anything hut contribute to the system) allow to leverage the capacities of a P2P system. While seeding is a natural idea for fllesharing or Video-on-Demand applications, it seems somehow counter-intuitive in the context of live streaming. This paper aims at describing the feasibility and performance of P2P live seeding. After a formal definition of "live seeding" and efficiency, we consider the theoretical performance of systems where the overhead is neglected. We then propose a realistic overhead model and extend the results for this model. The performance of a single seeder and a set of seeders are considered, as it is not always possible to perfectly aggregate individual efficiencies. In details, we provide an explicit upper bound of seeders' achievable efficiency in a P2P system with linear overhead. We also propose and study two simple mechanisms that allow to deploy a live seeding architecture while handling seeders aggregation, providing near-optimal seeding.

Optimal dissemination schemes have previously been studied for peer-to-peer live streaming applications. Live streaming being a delay-sensitive application, fine tuning of dissemination parameters is crucial. In this paper, we investigate optimal sizing of chunks, the units of data exchange, and probe sets, the number peers a given node probes before transmitting chunks. Chunk size can have significant impact on diffusion rate (chunk miss ratio), diffusion delay, and overhead. The size of the probe set can also affect these metrics, primarily through the choices available for chunk dissemination. We perform extensive simulations on the so-called random-peer, latest-useful dissemination scheme. Our results show that size does matter, with the optimal size being not too small in both cases.

In a distributed Video-on-Demand system, customers are in charge of storing the video catalog, and they actively participate in serving video requests generated by other customers. The design of such systems is driven by key constraints like customer upload and storage capacities, video popularity distribution, and so on. In this paper, we analyze by simulations the impact of: i) the video allocation technique (used for distributed storage) ii) the use of a cache that allows nodes to redistribute the video they are using iii) the use of static/dynamic algorithms for video distribution. Based on these results, we provide some guidelines for setting the system parameters: the use of cache strongly improves system performance; popularity based allocation techniques can be sensitive and bring little improvement; dynamic distribution algorithms are needed only in extreme scenarios while static ones are generally sufficient.

Acyclic preferences recently appeared as an elegant way to model many distributed systems. An acyclic instance admits a unique stable configuration, which can reveal the performance of the system. In this paper, we give the statistical properties of the stable configuration for three classes of acyclic preferences: node-based preferences, distance-based preferences, and random acyclic systems. Using random overlay graphs, we prove by means of a mean-field assumption and a fluid-limit technique that these systems have an asymptotically continuous independent rank distribution for a proper scaling, and the analytical solution is compared to simulations. These results provide a theoretical ground for validating the performance of bandwidth-based or proximity-based unstructured systems.

We consider the fully distributed video-on-demand problem, where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less between them. It is said to be scalable when Omega (n) videos can be distributively stored under the condition that any sequence of demands for these videos can always be satisfied. Our main result consists in establishing a threshold on the average upload bandwidth of a box, above which the system becomes scalable. We are thus interested in the normalized upload capacity u = upload bandwidth/video bitrate of a box. The number m of distinct videos stored in the system is called its catalog size. We show an upload capacity threshold of 1 for scalability in a homogeneous system, where all boxes have the same upload capacity. More precisely, a system with u1, an homogeneous system where all boxes have same upload capacity at least u admits a static allocation of m = Omega (n) videos into the boxes such that any adversarial sequence of video demands can be satisfied. Moreover, such an allocation can be obtained randomly with high probability. This result is generalized to a system of boxes that have heterogeneous upload capacities under some balancing conditions.

We investigate performance bounds of P2P systems by application of the law of bandwidth conservation. This approach is quite general and allows us to consider various sharing systems such as fixed-rate streaming, VoD-type streaming, and elastic file sharing. Starting from a general law of bandwidth conservation, we consider several specific cases that apply to various P2P systems. For dynamic systems with a stationary arrival process, we show that simple seeding policies result in regimes where the download rates are arbitrarily fast. We consider a case with equal download rate among all peers as well as cases where the download rate is a function of upload rates, inspired by BitTorrent's tit-for-tat policy. In particular, we show that the sustainable proportion of free-riders is closely related to the tit-for-tat parameter.

Several peer-to-peer systems for live streaming have been recently deployed (e.g. CoolStreaming, PPLive, SopCast). These all rely on distributed, epidemic-style dissemination mechanisms. Despite their popularity, the fundamental performance trade-offs of such mechanisms are still poorly understood. In this paper we propose several results that contribute to the understanding of such trade-offs.

In this paper, we analyze the stability of large scale superpeer networks against attack. Two different kinds of attacks namely deterministic and degree dependent attack have been introduced. We model the superpeer networks with the help of bimodal degree distribution and different attacks with the help of graph dynamics. It is interesting to observe from both theoretical and simulation results that peer degree plays the key role for maintaining stability of the superpeer networks in face of these two attacks.