Network monitoring and performance assessment: from statistical models to neural networks

Máster en Investigación e Innovación en Tecnologías de la Información y las ComunicacionesIn the last few years, computer networks have been playing a key role in many different fields. Companies have also evolved around the internet, getting advantage of the huge capacity of diffusion. Nevertheless, this also means that computer networks and IT systems have become a critical element for the business. In case of interruption or malfunction of the systems, this could result in devastating economic impact. In this light, it is necessary to provide models to properly evaluate and characterize the computer networks. Focusing on modeling, one has many different alternatives: from classical options based on statistic to recent alternatives based on machine learning and deep learning. In this work, we want to study the different models available for each context, paying attention to the advantage and disadvantages to provide the best solution for each case. To cover the majority of the spectrum, three cases have been studied: time-unaware phenomena, where we look at the bias-variance trade-off, time-dependent phenomena, where we pay attention the trends of the time series, and text processing to process attributes obtained by DPI. For each case, several alternatives have been studied and solutions have been tested both with synthetic data and real-world data, showing the successfulness of the proposa

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Natural language processing for web browsing analytics: Challenges, lessons learned, and opportunities

In an Internet arena where the search engines and other digital marketing firms’ revenues peak, other actors still have open opportunities to monetize their users’ data. After the convenient anonymization, aggregation, and agreement, the set of websites users visit may result in exploitable data for ISPs. Uses cover from assessing the scope of advertising campaigns to reinforcing user fidelity among other marketing approaches, as well as security issues. However, sniffers based on HTTP, DNS, TLS or flow features do not suffice for this task. Modern websites are designed for preloading and prefetching some contents in addition to embedding banners, social networks’ links, images, and scripts from other websites. This self-triggered traffic makes it confusing to assess which websites users visited on purpose. Moreover, DNS caches prevent some queries of actively visited websites to be even sent. On this limited input, we propose to handle such domains as words and the sequences of domains as documents. This way, it is possible to identify the visited websites by translating this problem to a text classification context and applying the most promising techniques of the natural language processing and neural networks fields. After applying different representation methods such as TF–IDF, Word2vec, Doc2vec, and custom neural networks in diverse scenarios and with several datasets, we can state websites visited on purpose with accuracy figures over 90%, with peaks close to 100%, being processes that are fully automated and free of any human parametrizationThis research has been partially funded by the Spanish State Research Agency under the project AgileMon (AEI PID2019-104451RBC21) and by the Spanish Ministry of Science, Innovation and Universities under the program for the training of university lecturers (Grant number: FPU19/05678

Effective temperature for finite systems

Under the Ansatz that the occupation times of a system with finitely many states are given by the Gibbs distribution, an effective temperature is uniquely determined (up to a choice of scale), and may be computed de novo, without any reference to a Hamiltonian for empirically accessible systems. As an example, the calculation of the effective temperature for a classical Bose gas is outlined and applied to the analysis of computer network traffic.Comment: 9 pages, 10 figure