Bruno Ordozgoiti

CogNet: A network management architecture featuring cognitive capabilities

It is expected that the fifth generation mobile networks (5G) will support both human-to-human and machine-to-machine communications, connecting up to trillions of devices and reaching formidable levels of complexity and traffic volume. This brings a new set of challenges for managing the network due to the diversity and the sheer size of the network. It will be necessary for the network to largely manage itself and deal with organisation, configuration, security, and optimisation issues. This paper proposes an architecture of an autonomic self-managing network based on Network Function Virtualization, which is capable of achieving or balancing objectives such as high QoS, low energy usage and operational efficiency. The main novelty of the architecture is the Cognitive Smart Engine introduced to enable Machine Learning, particularly (near) real-time learning, in order to dynamically adapt resources to the immediate requirements of the virtual network functions, while minimizing performance degradations to fulfill SLA requirements. This architecture is built within the CogNet European Horizon 2020 project, which refers to Cognitive Networks.

Can machine learning aid in delivering new use cases and scenarios in 5G

5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.

A Fast Iterative Algorithm for Improved Unsupervised Feature Selection

Dimensionality reduction is often a crucial step for the successfulapplication of machine learning and data mining methods. One way toachieve said reduction is feature selection. Due to the impossibilityof labelling many data sets, unsupervised approaches arefrequently the only option. The column subset selection problemtranslates naturally to this purpose, and has received considerableattention over the last few years, as it provides simple linear modelsfor data reconstruction. Existing methods, however, often achieveapproximation errors that are far from the optimum. In this paper wepresent a novel algorithm for column subset selection thatconsistently outperforms state-of-the-art methods in approximationerror. We present a series of key derivations that allow anefficient implementation, making it comparable in speed and in somecases faster than other algorithms. We also prove results that make itpossible to deal with huge matrices, which has strong implications for otheralgorithms of this type in the big data field. We validate our claimsthrough experiments on a wide variety of well-known data sets.

Massively Parallel Unsupervised Feature Selection on Spark

High dimensional data sets pose important challenges such as the curse of dimensionality and increased computational costs. Dimensionality reduction is therefore a crucial step for most data mining applications. Feature selection techniques allow us to achieve said reduction. However, it is nowadays common to deal with huge data sets, and most existing feature selection algorithms are designed to function in a centralized fashion, which makes them non scalable. Moreover, some of them require the selection process to be validated according to some target, which constrains their applicability to the supervised learning setting. In this paper we propose as novelty a parallel, scalable, exact implementation of an existing centralized, unsupervised feature selection algorithm on Spark, an efficient big data framework for large-scale distributed computation that outperforms MapReduce when applied to multi-pass algorithms. We validate the efficiency of the implementation using 1GB of real Internet traffic captured at a medium-sized ISP.

Forecasting short-term data center network traffic load with convolutional neural networks

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

Iterative column subset selection

Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction is feature selection. Due to the impossibility of labelling many data sets, unsupervised approaches are frequently the only option. The column subset selection problem translates naturally to this purpose and has received considerable attention over the last few years, as it provides simple linear models for low-rank data reconstruction. Recently, it was empirically shown that an iterative algorithm, which can be implemented efficiently, provides better subsets than other state-of-the-art methods. In this paper, we describe this algorithm and provide a more in-depth analysis. We carry out numerous experiments to gain insights on its behaviour and derive a simple bound for the norm recovered by the resulting matrix. To the best of our knowledge, this is the first theoretical result of this kind for this algorithm.

Probabilistic Leverage Scores for Parallelized Unsupervised Feature Selection

Dimensionality reduction is often crucial for the application of machine learning and data mining. Feature selection methods can be employed for this purpose, with the advantage of preserving interpretability. There exist unsupervised feature selection methods based on matrix factorization algorithms, which can help choose the most informative features in terms of approximation error. Randomized methods have been proposed recently to provide better theoretical guarantees and better approximation errors than their deterministic counterparts, but their computational costs can be significant when dealing with big, high dimensional data sets. Some existing randomized and deterministic approaches require the computation of the singular value decomposition in \(O(mn\min (m,n))\) time (for m samples and n features) for providing leverage scores. This compromises their applicability to domains of even moderately high dimensionality. In this paper we propose the use of Probabilistic PCA to compute the leverage scores in O(mnk) time, enabling the applicability of some of these randomized methods to large, high-dimensional data sets. We show that using this approach, we can rapidly provide an approximation of the leverage scores that is works well in this context. In addition, we offer a parallelized version over the emerging Resilient Distributed Datasets paradigm (RDD) on Apache Spark, making it horizontally scalable for enormous numbers of data instances. We validate the performance of our approach on different data sets comprised of real-world and synthetic data.

Feature Ranking and Selection for Big Data Sets

The availability of big data sets has led to the successful application of machine learning and data mining to problems that were previously unsolved. The use of these techniques, though, is rarely straightforward. High dimensionality is often one of the main obstacles that must be overcome before learning an adequate model or drawing useful conclusions from large amounts of data. Rank revealing matrix factorizations can help in addressing this problem, by permuting the columns of the input data so that linearly dependent and thus redundant ones are moved to the right. These factorizations, however, are designed to operate in a centralized fashion, requiring the input data to be loaded into main memory, which makes them inapplicable to large data sets. In this paper we prove that data sets comprised of a huge number of rows can be easily transformed into a compact square matrix that preserves the permutation yielded by rank revealing QR factorizations. This leads to a simple algorithm for running these factorizations on big data sets regardless of their number of rows. The nature of the transformation makes it also possible to deal with high dimensional data with a controlled loss of precision. We offer experimental results showing that our method can provide improvements for the k-means algorithm, both in clustering results and in running time.