MOSEL: Inference Serving Using Dynamic Modality Selection

Rapid advancements over the years have helped machine learning models reach previously hard-to-achieve goals, sometimes even exceeding human capabilities. However, to attain the desired accuracy, the model sizes and in turn their computational requirements have increased drastically. Thus, serving predictions from these models to meet any target latency and cost requirements of applications remains a key challenge, despite recent work in building inference-serving systems as well as algorithmic approaches that dynamically adapt models based on inputs. In this paper, we introduce a form of dynamism, modality selection, where we adaptively choose modalities from inference inputs while maintaining the model quality. We introduce MOSEL, an automated inference serving system for multi-modal ML models that carefully picks input modalities per request based on user-defined performance and accuracy requirements. MOSEL exploits modality configurations extensively, improving system throughput by 3.6$\times$ with an accuracy guarantee and shortening job completion times by 11$\times$

Multifractal Internet Traffic Model and Active Queue Management

We propose a multilevel (hierarchical) ON/OFF model to simultaneously capture the mono/multifractal behavior of Internet traffic. Parameter estimation methods are developed and applied to estimate the model parameters from real traces. Wavelet analysis and simulation results show that the synthetic traffic (using this new model with estimated parameters) and real traffic share the same statistical properties and queuing behaviors. Based on this model and its statistical properties, as described by the Logscale diagram of traces, we propose an efficient method to predict the queuing behavior of FIFO and RED queues. In order to satisfy a given delay and jitter requirement for real time connections, and to provide high goodput and low packet loss for non-real time connections, we also propose a parallel virtual queue control structure to offer differential quality of services. This new queue control structure is modeled and analyzed as a regular nonlinear dynamic system. The conditions for system stability and optimization are found (under certain simplifying assumptions) and discussed. The theoretical stationary distribution of queue length is validated by simulation

Design and analysis of optimal resource allocation policies in wireless networks

Ph.DDOCTOR OF PHILOSOPH

Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models

Simulating a large-scale network like the Internet is a challenging undertaking because of the sheer volume of its traffic. Packet-oriented representation provides high-fidelity details but is computationally expensive; fluid-oriented representation offers high simulation efficiency at the price of losing packet-level details. Multi-resolution modeling techniques exploit the advantages of both representations by integrating them in the same simulation framework. This dissertation presents solutions to the problems regarding the efficiency, accuracy, and scalability of the traffic simulation models in this framework. The ``ripple effect\u27\u27 is a well-known problem inherent in event-driven fluid-oriented traffic simulation, causing explosion of fluid rate changes. Integrating multi-resolution traffic representations requires estimating arrival rates of packet-oriented traffic, calculating the queueing delay upon a packet arrival, and computing packet loss rate under buffer overflow. Real time simulation of a large or ultra-large network demands efficient background traffic simulation. The dissertation includes a rate smoothing technique that provably mitigates the ``ripple effect\u27\u27, an accurate and efficient approach that integrates traffic models at multiple abstraction levels, a sequential algorithm that achieves real time simulation of the coarse-grained traffic in a network with 3 tier-1 ISP (Internet Service Provider) backbones using an ordinary PC, and a highly scalable parallel algorithm that simulates network traffic at coarse time scales

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Software-Defined Infrastructure for IoT-based Energy Systems

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT devices need programmability and need to provide software controls in order to manage them efficiently. Further, it will need data-driven modeling techniques to process and analyze a vast amount of data from heterogeneous devices to derive actionable insights. My thesis explores the problems posed by software-defined IoT energy infrastructure. I present four techniques that use systems and machine learning principles to design, analyze and deploy the next generation of smart IoT energy systems. First, I discuss how current state-of-the-art LIDAR-based approaches in identifying ideal locations on rooftops for deploying energy systems such as solar do not scale to many regions of the world. To address the challenges, I propose DeepRoof, a data-driven approach that uses deep learning to estimate the solar potential of roofs using satellite imagery and identify ideal locations for installation. We evaluate our approach on different types of roof and show that our technique is comparable to LIDAR-based methods. Second, I study how excessive solar can cause problems in the grid and examine how programmatic control of the solar output can prevent congestion in the electric grid. Further, I present a decentralized approach that can control the solar arrays in a grid-friendly manner. Also, my approach provides flexible control of solar output, and I show that such mechanisms allow for higher solar penetration in the grid. Third, I discuss the challenges in community-owned (and shared) distributed energy resources that do not provide independent control to users. To do so, I propose vSolar, an approach to virtualize the solar arrays and energy storage that allows independent control. Further, I show how using vSolar users can exercise independent control, implement their custom energy sharing policies, and reduce energy costs through energy trading. Finally, I present the challenges, and the high throughput needs to enable a peer-to-peer energy trading platform using permissioned blockchains. I propose FabricPlus, an enhanced Hyperledger Fabric blockchain, that contains a series of optimizations to enable high throughput transactions. FabricPlus increases the transaction throughput many folds, without requiring any changes to its external interfaces. I also show considerable performance improvement over the baseline Fabric

Intelligence in 5G networks

Over the past decade, Artificial Intelligence (AI) has become an important part of our daily lives; however, its application to communication networks has been partial and unsystematic, with uncoordinated efforts that often conflict with each other. Providing a framework to integrate the existing studies and to actually build an intelligent network is a top research priority. In fact, one of the objectives of 5G is to manage all communications under a single overarching paradigm, and the staggering complexity of this task is beyond the scope of human-designed algorithms and control systems. This thesis presents an overview of all the necessary components to integrate intelligence in this complex environment, with a user-centric perspective: network optimization should always have the end goal of improving the experience of the user. Each step is described with the aid of one or more case studies, involving various network functions and elements. Starting from perception and prediction of the surrounding environment, the first core requirements of an intelligent system, this work gradually builds its way up to showing examples of fully autonomous network agents which learn from experience without any human intervention or pre-defined behavior, discussing the possible application of each aspect of intelligence in future networks

Prévision du trafic Internet : modèles et applications

Avec l'essor de la métrologie de l'Internet, la prévision du trafic s'est imposée comme une de ses branches les plus importantes. C'est un outil puissant qui permet d'aider à la conception, la mise en place et la gestion des réseaux ainsi qu'à l'ingénierie du trafic et le contrôle des paramètres de qualité de service. L'objectif de cette thèse est d'étudier les techniques de prévision et d'évaluer la performance des modèles de prévision et de les appliquer pour la gestion des files d'attente et le contrôle du taux de perte dans les réseaux à commutation de rafales. Ainsi, on analyse les différents paramètres qui permettent d'améliorer la performance de la prévision en termes d'erreur. Les paramètres étudiés sont : la quantité de données nécessaires pour définir les paramètres du modèle, leur granularité, le nombre d'entrées du modèle ainsi que les caractéristiques du trafic telles que sa variance et la distribution de la taille des paquets. Nous proposons aussi une technique d'échantillonnage baptisée échantillonnage basé sur le maximum (Max-Based Sampling - MBS). Nous prouvons son efficacité pour améliorer la performance de la prévision et préserver l'auto-similarité et la dépendance à long terme du trafic. \ud Le travail porte aussi sur l'exploitation de la prévision du trafic pour la gestion du trafic et le contrôle du taux de perte dans les réseaux à commutation de rafales. Ainsi, nous proposons un nouveau mécanisme de gestion de files d'attente, baptisé α_SNFAQM, qui est basé sur la prévision du trafic. Ce mécanisme permet de stabiliser la taille de la file d'attente et par suite, contrôler les délais d'attente des paquets. Nous proposons aussi une nouvelle technique qui permet de garantir la qualité de service dans les réseaux à commutation de rafales en termes de taux de perte. Elle combine entre la modélisation, la prévision du trafic et les systèmes asservis avec feedback. Elle permet de contrôler efficacement le taux de perte des rafales pour chaque classe de service. Le modèle est ensuite amélioré afin d'éviter les feedbacks du réseau en utilisant la prévision du taux de perte au niveau TCP. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Modélisation et prévision du trafic, techniques d'échantillonnage, gestion des files d'attente, réseaux à commutation de rafales, contrôle du taux de perte, qualité de service, l'automatique