Model-driven dual caching For nomadic service-oriented architecture clients

Mobile devices have evolved over the years from resource constrained devices that supported only the most basic tasks to powerful handheld computing devices. However, the most significant step in the evolution of mobile devices was the introduction of wireless connectivity which enabled them to host applications that require internet connectivity such as email, web browsers and maybe most importantly smart/rich clients. Being able to host smart clients allows the users of mobile devices to seamlessly access the Information Technology (IT) resources of their organizations. One increasingly popular way of enabling access to IT resources is by using Web Services (WS). This trend has been aided by the rapid availability of WS packages/tools, most notably the efforts of the Apache group and Integrated Development Environment (IDE) vendors. But the widespread use of WS raises questions for users of mobile devices such as laptops or PDAs; how and if they can participate in WS. Unlike their “wired” counterparts (desktop computers and servers) they rely on a wireless network that is characterized by low bandwidth and unreliable connectivity.The aim of this thesis is to enable mobile devices to host Web Services consumers. It introduces a Model-Driven Dual Caching (MDDC) approach to overcome problems arising from temporarily loss of connectivity and fluctuations in bandwidth

Improved Designs for Application Virtualization

We propose solutions for application virtualization to mitigate the performance loss in streaming and browser-based applications. For the application streaming, we propose a solution which keeps operating system components and application software at the server and streams them to the client side for execution. This architecture minimizes the components managed at the clients and improves the platform-level incompatibility. The runtime performance of application streaming is significantly reduced when the required code is not properly available on the client side. To mitigate this issue and boost the runtime performance, we propose prefetching, i.e., speculatively delivering code blocks to the clients in advance. The probability model on which our prefetch method is based may be very large. To manage such a probability model and the associated hardware resources, we perform an information gain analysis. We draw two lower bounds of the information gain brought by an attribute set required to achieve a prefetch hit rate. We organize the probability model as a look-up table: LUT). Similar to the memory hierarchy which is widely used in the computing field, we separate the single LUT into two-level, hierarchical LUTs. To separate the entries without sorting all entries, we propose an entropy-based fast LUT separation algorithm which utilizes the entropy as an indicator. Since the domain of the attribute can be much larger than the addressable space of a virtual memory system, we need an efficient way to allocate each LUT\u27s entry in a limited memory address space. Instead of using expensive CAM, we use a hash function to convert the attribute values into addresses. We propose an improved version of the Pearson hashing to reduce the collision rate with little extra complexity. Long interactive delays due to network delays are a significant drawback for the browser-based application virtualization. To address this, we propose a distributed infrastructure arrangement for browser-based application virtualization which reduces the average communication distance among servers and clients. We investigate a hand-off protocol to deal with the user mobility in the browser-based application virtualization. Analyses and simulations for information-based prefetching and for mobile applications are provided to quantify the benefits of the proposed solutions

Sistemas interativos e distribuídos para telemedicina

doutoramento Ciências da ComputaçãoDurante as últimas décadas, as organizações de saúde têm vindo a adotar continuadamente as tecnologias de informação para melhorar o funcionamento dos seus serviços. Recentemente, em parte devido à crise financeira, algumas reformas no sector de saúde incentivaram o aparecimento de novas soluções de telemedicina para otimizar a utilização de recursos humanos e de equipamentos. Algumas tecnologias como a computação em nuvem, a computação móvel e os sistemas Web, têm sido importantes para o sucesso destas novas aplicações de telemedicina. As funcionalidades emergentes de computação distribuída facilitam a ligação de comunidades médicas, promovem serviços de telemedicina e a colaboração em tempo real. Também são evidentes algumas vantagens que os dispositivos móveis podem introduzir, tais como facilitar o trabalho remoto a qualquer hora e em qualquer lugar. Por outro lado, muitas funcionalidades que se tornaram comuns nas redes sociais, tais como a partilha de dados, a troca de mensagens, os fóruns de discussão e a videoconferência, têm o potencial para promover a colaboração no sector da saúde. Esta tese teve como objetivo principal investigar soluções computacionais mais ágeis que permitam promover a partilha de dados clínicos e facilitar a criação de fluxos de trabalho colaborativos em radiologia. Através da exploração das atuais tecnologias Web e de computação móvel, concebemos uma solução ubíqua para a visualização de imagens médicas e desenvolvemos um sistema colaborativo para a área de radiologia, baseado na tecnologia da computação em nuvem. Neste percurso, foram investigadas metodologias de mineração de texto, de representação semântica e de recuperação de informação baseada no conteúdo da imagem. Para garantir a privacidade dos pacientes e agilizar o processo de partilha de dados em ambientes colaborativos, propomos ainda uma metodologia que usa aprendizagem automática para anonimizar as imagens médicasDuring the last decades, healthcare organizations have been increasingly relying on information technologies to improve their services. At the same time, the optimization of resources, both professionals and equipment, have promoted the emergence of telemedicine solutions. Some technologies including cloud computing, mobile computing, web systems and distributed computing can be used to facilitate the creation of medical communities, and the promotion of telemedicine services and real-time collaboration. On the other hand, many features that have become commonplace in social networks, such as data sharing, message exchange, discussion forums, and a videoconference, have also the potential to foster collaboration in the health sector. The main objective of this research work was to investigate computational solutions that allow us to promote the sharing of clinical data and to facilitate the creation of collaborative workflows in radiology. By exploring computing and mobile computing technologies, we have designed a solution for medical imaging visualization, and developed a collaborative system for radiology, based on cloud computing technology. To extract more information from data, we investigated several methodologies such as text mining, semantic representation, content-based information retrieval. Finally, to ensure patient privacy and to streamline the data sharing in collaborative environments, we propose a machine learning methodology to anonymize medical images

MDSA, MULTI DECISION SCHEDULING ALGORITHM FOR UE ENERGY POWER SAVING ON MOBILE NETWORKS

Nowadays general different kind of recurrent network applications on mobile phones are like: news feed, podcast and e-mail which mostly run in the background and are a significant source of power consumption on battery limited mobile phones. Theory of scheduling such applications by prioritizing and evaluating step by step based on many conditions (or even parameters, timers) like radio network parameters, timeout values, coverage conditions or RSSI, switching between 2G and 3G on packet data, etc is our main focus on this paper. Cellular network providers typically try to control these timeout values, though some mobile devices use a technique called fast dormancy in order to reduce the time out duration which mostly results in huge power consumption for end user. The duration of this timeout, which ranges from a few seconds to ten seconds or more, is chosen to balance the cost of signaling for resource allocation to move a radio into active state (and the resulting latency and energy costs on the device) and the wasted resources due to maintaining a radio unnecessarily in active state. We also illustrate the significant energy savings that can be achieved via scheduling of recurrent mobile phone applications considering some network parameters, conditions and also user activity time (clock time) and phone battery condition as an add. These kinds of applications such as email syncing, facebook or photo uploads can defer communication, up to a point, without sacrificing service and user perception. Other applications such as on-demand streaming can prefetch content in anticipation of future need and this will not be considered for now on our study. The other very common service for mobile users is voice and by transmitting data when a call is active brings an extension on mobile phones battery life

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain

새로운 메모리 기술을 기반으로 한 메모리 시스템 설계 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 최기영.Performance and energy efficiency of modern computer systems are largely dominated by the memory system. This memory bottleneck has been exacerbated in the past few years with (1) architectural innovations for improving the efficiency of computation units (e.g., chip multiprocessors), which shift the major cause of inefficiency from processors to memory, and (2) the emergence of data-intensive applications, which demands a large capacity of main memory and an excessive amount of memory bandwidth to efficiently handle such workloads. In order to address this memory wall challenge, this dissertation aims at exploring the potential of emerging memory technologies and designing a high-performance, energy-efficient memory hierarchy that is aware of and leverages the characteristics of such new memory technologies. The first part of this dissertation focuses on energy-efficient on-chip cache design based on a new non-volatile memory technology called Spin-Transfer Torque RAM (STT-RAM). When STT-RAM is used to build on-chip caches, it provides several advantages over conventional charge-based memory (e.g., SRAM or eDRAM), such as non-volatility, lower static power, and higher density. However, simply replacing SRAM caches with STT-RAM rather increases the energy consumption because write operations of STT-RAM are slower and more energy-consuming than those of SRAM. To address this challenge, we propose four novel architectural techniques that can alleviate the impact of inefficient STT-RAM write operations on system performance and energy consumption. First, we apply STT-RAM to instruction caches (where write operations are relatively infrequent) and devise a power-gating mechanism called LASIC, which leverages the non-volatility of STT-RAM to turn off STT-RAM instruction caches inside small loops. Second, we propose lower-bits cache, which exploits the narrow bit-width characteristics of application data by caching frequent bit-flips at lower bits in a small SRAM cache. Third, we present prediction hybrid cache, an SRAM/STT-RAM hybrid cache whose block placement between SRAM and STT-RAM is determined by predicting the write intensity of each cache block with a new hardware structure called write intensity predictor. Fourth, we propose DASCA, which predicts write operations that can bypass the cache without incurring extra cache misses (called dead writes) and lets the last-level cache bypass such dead writes to reduce write energy consumption. The second part of this dissertation architects intelligent main memory and its host architecture support based on logic-enabled DRAM. Traditionally, main memory has served the sole purpose of storing data because the extra manufacturing cost of implementing rich functionality (e.g., computation) on a DRAM die was unacceptably high. However, the advent of 3D die stacking now provides a practical, cost-effective way to integrate complex logic circuits into main memory, thereby opening up the possibilities for intelligent main memory. For example, it can be utilized to implement advanced memory management features (e.g., scheduling, power management, etc.) inside memoryit can be also used to offload computation to main memory, which allows us to overcome the memory bandwidth bottleneck caused by narrow off-chip channels (commonly known as processing-in-memory or PIM). The remaining questions are what to implement inside main memory and how to integrate and expose such new features to existing systems. In order to answer these questions, we propose four system designs that utilize logic-enabled DRAM to improve system performance and energy efficiency. First, we utilize the existing logic layer of a Hybrid Memory Cube (a commercial logic-enabled DRAM product) to (1) dynamically turn off some of its off-chip links by monitoring the actual bandwidth demand and (2) integrate prefetch buffer into main memory to perform aggressive prefetching without consuming off-chip link bandwidth. Second, we propose a scalable accelerator for large-scale graph processing called Tesseract, in which graph processing computation is offloaded to specialized processors inside main memory in order to achieve memory-capacity-proportional performance. Third, we design a low-overhead PIM architecture for near-term adoption called PIM-enabled instructions, where PIM operations are interfaced as cache-coherent, virtually-addressed host processor instructions that can be executed either by the host processor or in main memory depending on the data locality. Fourth, we propose an energy-efficient PIM system called aggregation-in-memory, which can adaptively execute PIM operations at any level of the memory hierarchy and provides a fully automated compiler toolchain that transforms existing applications to use PIM operations without programmer intervention.Chapter 1 Introduction 1 1.1 Inefficiencies in the Current Memory Systems 2 1.1.1 On-Chip Caches 2 1.1.2 Main Memory 2 1.2 New Memory Technologies: Opportunities and Challenges 3 1.2.1 Energy-Efficient On-Chip Caches based on STT-RAM 3 1.2.2 Intelligent Main Memory based on Logic-Enabled DRAM 6 1.3 Dissertation Overview 9 Chapter 2 Previous Work 11 2.1 Energy-Efficient On-Chip Caches based on STT-RAM 11 2.1.1 Hybrid Caches 11 2.1.2 Volatile STT-RAM 13 2.1.3 Redundant Write Elimination 14 2.2 Intelligent Main Memory based on Logic-Enabled DRAM 15 2.2.1 PIM Architectures in the 1990s 15 2.2.2 Modern PIM Architectures based on 3D Stacking 15 2.2.3 Modern PIM Architectures on Memory Dies 17 Chapter 3 Loop-Aware Sleepy Instruction Cache 19 3.1 Architecture 20 3.1.1 Loop Cache 21 3.1.2 Loop-Aware Sleep Controller 22 3.2 Evaluation and Discussion 24 3.2.1 Simulation Environment 24 3.2.2 Energy 25 3.2.3 Performance 27 3.2.4 Sensitivity Analysis 27 3.3 Summary 28 Chapter 4 Lower-Bits Cache 29 4.1 Architecture 29 4.2 Experiments 32 4.2.1 Simulator and Cache Model 32 4.2.2 Results 33 4.3 Summary 34 Chapter 5 Prediction Hybrid Cache 35 5.1 Problem and Motivation 37 5.1.1 Problem Definition 37 5.1.2 Motivation 37 5.2 Write Intensity Predictor 38 5.2.1 Keeping Track of Trigger Instructions 39 5.2.2 Identifying Hot Trigger Instructions 40 5.2.3 Dynamic Set Sampling 41 5.2.4 Summary 42 5.3 Prediction Hybrid Cache 43 5.3.1 Need for Write Intensity Prediction 43 5.3.2 Organization 43 5.3.3 Operations 44 5.3.4 Dynamic Threshold Adjustment 45 5.4 Evaluation Methodology 48 5.4.1 Simulator Configuration 48 5.4.2 Workloads 50 5.5 Single-Core Evaluations 51 5.5.1 Energy Consumption and Speedup 51 5.5.2 Energy Breakdown 53 5.5.3 Coverage and Accuracy 54 5.5.4 Sensitivity to Write Intensity Threshold 55 5.5.5 Impact of Dynamic Set Sampling 55 5.5.6 Results for Non-Write-Intensive Workloads 56 5.6 Multicore Evaluations 57 5.7 Summary 59 Chapter 6 Dead Write Prediction Assisted STT-RAM Cache 61 6.1 Motivation 62 6.1.1 Energy Impact of Inefficient Write Operations 62 6.1.2 Limitations of Existing Approaches 63 6.1.3 Potential of Dead Writes 64 6.2 Dead Write Classification 65 6.2.1 Dead-on-Arrival Fills 65 6.2.2 Dead-Value Fills 66 6.2.3 Closing Writes 66 6.2.4 Decomposition 67 6.3 Dead Write Prediction Assisted STT-RAM Cache Architecture 68 6.3.1 Dead Write Prediction 68 6.3.2 Bidirectional Bypass 71 6.4 Evaluation Methodology 72 6.4.1 Simulation Configuration 72 6.4.2 Workloads 74 6.5 Evaluation for Single-Core Systems 75 6.5.1 Energy Consumption and Speedup 75 6.5.2 Coverage and Accuracy 78 6.5.3 Sensitivity to Signature 78 6.5.4 Sensitivity to Update Policy 80 6.5.5 Implications of Device-/Circuit-Level Techniques for Write Energy Reduction 80 6.5.6 Impact of Prefetching 80 6.6 Evaluation for Multi-Core Systems 81 6.6.1 Energy Consumption and Speedup 81 6.6.2 Application to Inclusive Caches 83 6.6.3 Application to Three-Level Cache Hierarchy 84 6.7 Summary 85 Chapter 7 Link Power Management for Hybrid Memory Cubes 87 7.1 Background and Motivation 88 7.1.1 Hybrid Memory Cube 88 7.1.2 Motivation 89 7.2 HMC Link Power Management 91 7.2.1 Link Delay Monitor 91 7.2.2 Power State Transition 94 7.2.3 Overhead 95 7.3 Two-Level Prefetching 95 7.4 Application to Multi-HMC Systems 97 7.5 Experiments 98 7.5.1 Methodology 98 7.5.2 Link Energy Consumption and Speedup 100 7.5.3 HMC Energy Consumption 102 7.5.4 Runtime Behavior of LPM 102 7.5.5 Sensitivity to Slowdown Threshold 104 7.5.6 LPM without Prefetching 104 7.5.7 Impact of Prefetching on Link Traffic 105 7.5.8 On-Chip Prefetcher Aggressiveness in 2LP 107 7.5.9 Tighter Off-Chip Bandwidth Margin 107 7.5.10 Multithreaded Workloads 108 7.5.11 Multi-HMC Systems 109 7.6 Summary 111 Chapter 8 Tesseract PIM System for Parallel Graph Processing 113 8.1 Background and Motivation 115 8.1.1 Large-Scale Graph Processing 115 8.1.2 Graph Processing on Conventional Systems 117 8.1.3 Processing-in-Memory 118 8.2 Tesseract Architecture 119 8.2.1 Overview 119 8.2.2 Remote Function Call via Message Passing 122 8.2.3 Prefetching 124 8.2.4 Programming Interface 126 8.2.5 Application Mapping 127 8.3 Evaluation Methodology 128 8.3.1 Simulation Configuration 128 8.3.2 Workloads 129 8.4 Evaluation Results 130 8.4.1 Performance 130 8.4.2 Iso-Bandwidth Comparison 133 8.4.3 Execution Time Breakdown 134 8.4.4 Prefetch Efficiency 134 8.4.5 Scalability 135 8.4.6 Effect of Higher Off-Chip Network Bandwidth 136 8.4.7 Effect of Better Graph Distribution 137 8.4.8 Energy/Power Consumption and Thermal Analysis 138 8.5 Summary 139 Chapter 9 PIM-Enabled Instructions 141 9.1 Potential of ISA Extensions as the PIM Interface 143 9.2 PIM Abstraction 145 9.2.1 Operations 145 9.2.2 Memory Model 147 9.2.3 Software Modification 148 9.3 Architecture 148 9.3.1 Overview 148 9.3.2 PEI Computation Unit (PCU) 149 9.3.3 PEI Management Unit (PMU) 150 9.3.4 Virtual Memory Support 153 9.3.5 PEI Execution 153 9.3.6 Comparison with Active Memory Operations 154 9.4 Target Applications for Case Study 155 9.4.1 Large-Scale Graph Processing 155 9.4.2 In-Memory Data Analytics 156 9.4.3 Machine Learning and Data Mining 157 9.4.4 Operation Summary 157 9.5 Evaluation Methodology 158 9.5.1 Simulation Configuration 158 9.5.2 Workloads 159 9.6 Evaluation Results 159 9.6.1 Performance 160 9.6.2 Sensitivity to Input Size 163 9.6.3 Multiprogrammed Workloads 164 9.6.4 Balanced Dispatch: Idea and Evaluation 165 9.6.5 Design Space Exploration for PCUs 165 9.6.6 Performance Overhead of the PMU 167 9.6.7 Energy, Area, and Thermal Issues 167 9.7 Summary 168 Chapter 10 Aggregation-in-Memory 171 10.1 Motivation 173 10.1.1 Rethinking PIM for Energy Efficiency 173 10.1.2 Aggregation as PIM Operations 174 10.2 Architecture 176 10.2.1 Overview 176 10.2.2 Programming Model 177 10.2.3 On-Chip Caches 177 10.2.4 Coherence and Consistency 181 10.2.5 Main Memory 181 10.2.6 Potential Generalization Opportunities 183 10.3 Compiler Support 184 10.4 Contributions over Prior Art 185 10.4.1 PIM-Enabled Instructions 185 10.4.2 Parallel Reduction in Caches 187 10.4.3 Row Buffer Locality of DRAM Writes 188 10.5 Target Applications 188 10.6 Evaluation Methodology 190 10.6.1 Simulation Configuration 190 10.6.2 Hardware Overhead 191 10.6.3 Workloads 192 10.7 Evaluation Results 192 10.7.1 Energy Consumption and Performance 192 10.7.2 Dynamic Energy Breakdown 196 10.7.3 Comparison with Aggressive Writeback 197 10.7.4 Multiprogrammed Workloads 198 10.7.5 Comparison with Intrinsic-based Code 198 10.8 Summary 199 Chapter 11 Conclusion 201 11.1 Energy-Efficient On-Chip Caches based on STT-RAM 202 11.2 Intelligent Main Memory based on Logic-Enabled DRAM 203 Bibliography 205 요약 227Docto

SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS

A finales de los años noventa, y al comienzo del nuevo milenio, las redes inalámbricas han evolucionado bastante, pasando de ser sólo una tecnología prometedora para convertirse en un requisito para las actividades cotidianas en las sociedades desarrolladas. La infraestructura de transporte también ha evolucionado, ofreciendo comunicación a bordo para mejorar la seguridad vial y el acceso a contenidos de información y entretenimiento. Los requisitos de los usuarios finales se han hecho dependientes de la tecnología, lo que significa que sus necesidades de conectividad han aumentado debido a los diversos requisitos de las aplicaciones que se ejecutan en sus dispositivos móviles, tales como tabletas, teléfonos inteligentes, ordenadores portátiles o incluso ordenadores de abordo (On-Board Units (OBUs)) dentro de los vehículos. Para cumplir con dichos requisitos de conectividad, y teniendo en cuenta las diferentes redes inalámbricas disponibles, es necesario adoptar técnicas de Vertical Handover (VHO) para cambiar de red de forma transparente y sin necesidad de intervención del usuario. El objetivo de esta tesis es desarrollar algoritmos de decisión (Vertical Handover Decision Algorithms (VHDAs)) eficientes y escalables, optimizados para el contexto de las redes vehiculares. En ese sentido se ha propuesto, desarrollado y probado diferentes algoritmos de decisión basados en la infraestructura disponible en las actuales, y probablemente en las futuras, redes inalámbricas y redes vehiculares. Para ello se han combinado diferentes técnicas, métodos computacionales y modelos matemáticos, con el fin de garantizar una conectividad apropiada, y realizando el handover hacia las redes más adecuadas de manera a cumplir tanto con los requisitos de los usuarios como los requisitos de las aplicaciones. Con el fin de evaluar el contexto, se han utilizado diferentes herramientas para obtener información variada, como la disponibilidad de la red, el estado de la red, la geolocalizaciónMárquez Barja, JM. (2012). SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17869Palanci