Moez Draief

Placing dynamic content in caches with small population

This paper addresses a fundamental limitation for the adoption of caching for wireless access networks due to small population sizes. This shortcoming is due to two main challenges: making timely estimates of varying content popularity and inferring popular content from small samples. We propose a framework which alleviates such limitations. To timely estimate varying popularity in a context of a single cache we propose an Age-Based Threshold (ABT) policy which caches all contents requested more times than a threshold N (τ), where τ is the content age. We show that ABT is asymptotically hit rate optimal in the many contents regime, which allows us to obtain the first characterization of the optimal performance of a caching system in a dynamic context. We then address small sample sizes focusing on L local caches and one global cache. On the one hand we show that the global cache learns L times faster by aggregating all requests from local caches, which improves hit rates. On the other hand, aggregation washes out local characteristics of correlated traffic which penalizes hit rate. This motivates coordination mechanisms which combine global learning of popularity scores in clusters and Least-Recently-Used (LRU) policy with prefetching.

A diffusion kernel LMS algorithm for nonlinear adaptive networks

This work presents a distributed algorithm for nonlinear adaptive learning. In particular, a set of nodes obtain measurements, sequentially one per time step, which are related via a nonlinear function; their goal is to collectively minimize a cost function by employing a diffusion based Kernel Least Mean Squares (KLMS). The algorithm follows the Adapt Then Combine mode of cooperation. Moreover, the theoretical properties of the algorithm are studied and it is proved that under certain assumptions the algorithm suffers a no regret bound. Finally, comparative experiments verify that the proposed scheme outperforms other variants of the LMS.

A method to reconstruct coverage loss maps based on matrix completion and adaptive sampling

Accurate coverage maps are an important tool for network planning and operation but it is often impossible to obtain these maps completely from measurements. In this paper we describe two new methods that enable operators to minimize the cost for obtaining a complete coverage map at high accuracy. Our first method applies the Singular Value Thresholding (SVT) algorithm to reconstruct a complete map from a sparse matrix of coverage data. We then use the Query by Committee (QbC) rationale to identify the areas where further measurements would maximize accuracy of the completed map. This second method allows operators to plan their drive tests such that a given budget is spent at highest efficiency. Our numerical examples illustrate that our proposed completion technique outperforms relevant state of the art and that QbC further enhances reconstruction accuracy.

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Probabilistic argumentation and neuro-argumentative systems offer new computational perspectives for the theory and applications of argumentation, but their principled construction involves two entangled problems. On the one hand, probabilistic argumentation aims at combining the quantitative uncertainty addressed by probability theory with the qualitative uncertainty of argumentation, but probabilistic dependences amongst arguments as well as learning are usually neglected. On the other hand, neuro-argumentative systems offer the opportunity to couple the computational advantages of learning and massive parallel computation from neural networks with argumentative reasoning and explanatory abilities, but the relation of probabilistic argumentation frameworks with these systems has been ignored so far. Towards the construction of neuro-argumentative systems based on probabilistic argumentation, we associate a model of abstract argumentation and the graphical model of Boltzmann machines (BMs) in order to (i...

Speeding Up Cover Time of Sparse Graphs Using Local Knowledge

We analyse the cover time of a random walk on a random graph of a given degree sequence. Weights are assigned to the edges of the graph using a certain type of scheme that uses only local degree knowledge. This biases the transitions of the walk towards lower degree vertices. We demonstrate that, with high probability, the cover time is at most ((1+o(1))frac{d-1}{d-2}8nlog n), where d is the minimum degree. This is in contrast to the precise cover time of ((1+o(1))frac{d-1}{d-2}frac{theta }{d} nlog n) (with high probability) given in [1] for a simple (i.e., unbiased) random walk on the same graph model. Here (theta ) is the average degree and since the ratio (theta /d) can be arbitrarily large, or go to infinity with n, we see that the scheme can give an unbounded speed up for sparse graphs.

Robust online matrix completion on graphs

We study online robust matrix completion on graphs. At each iteration a vector with some entries missing is revealed and our goal is to reconstruct it by identifying the underlying low-dimensional subspace from which the vectors are drawn. We assume there is an underlying graph structure to the data, that is, the components of each vector correspond to nodes of a certain known and fixed graph, and their values are related accordingly. We propose an algorithm that exploits the graph to reconstruct the incomplete data, in the scenario where there is outlier noise. The theoretical properties of the algorithms are studied and numerical experiments using both synthetic and real world datasets verify the improved performance of the proposed technique compared to other state of the art algorithms.

Admission control with online algorithms in SDN

By offloading the control plane to powerful computing platforms running on commodity hardware, Software Defined Networking (SDN) unleashes the potential to operate computation intensive machine learning tools and solve complex optimization problems in a centralized fashion. This paper studies such an opportunity under the framework of the centralized SDN Admission Control (AC) problem. We first review and adapt some of the key AC algorithms from the literature, and evaluate their performance under realistic settings. We then propose to take a step further and build an AC meta-algorithm that is able to track the best AC algorithm under unknown traffic conditions. To this aim, we exploit a machine learning technique called Strategic Expert meta-Algorithm (SEA).

Online Bandwidth Calendaring: On-the-fly admission, scheduling, and path computation

The centralized control in Software Defined Networks paves the way for new services like Bandwidth Calendaring (BWC), where the possibility to shift temporally future bandwidth requests allows to efficiently use network resources. Assuming perfect knowledge of the calendar for all future bandwidth reservations is unrealistic. In this paper, we study the online version of the BWC problem presented in [1], where for unpredictable incoming demands an admission decision, scheduling and path allocation must be taken instantaneously. We design an algorithm for solving the online version of the BWC problem and proposes two heuristic approaches to exploit the scheduling flexibility of demands. Our numerical results reveal that the proposed solution approach outperforms state-of-the art methods by up to 70% in terms of accepted traffic.

An efficient online Adaptive Sampling strategy for Matrix Completion

Matrix Completion (MC) i.e., estimating the missing values of an unknown matrix based on limited information, has been a prominent topic of study in the last decades. In this paper, we focus more precisely on a little-known aspect of MC, namely how the recommendation of new entries can help improve the accuracy of the reconstruction. We present an efficient online algorithm to solve the MC task, and propose an Adaptive Sampling under Smoothness Assumption (AdSSA) strategy, which is suitable for operation on smooth low-rank matrices. This technique is able to predict iteratively which entries are the most informative. Our numerical examples illustrate that AdSSA performs significantly better than the Uniform Random Sampling (URS) and the Query by Committee (QbC). In addition, AdSSA algorithm can be straightforwardly implemented in an online and efficient manner, which constitutes another advantage.

Online experts for admission control in SDN

SDN unleashes the potential to perform computational intensive machine learning algorithms to solve complex routing problems. This demo presents an architecture for the SDN controller that integrates several online routing algorithms for the real-time admission control of new connection requests. The demonstrator permits to compare the evolution of the network according to the admission decisions taken by different online algorithms and to simulate future scenarios to support strategic decisions aimed at improving the infrastructure.