THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

Durant les quatre dernières décennies, la miniaturisation a permis la diffusion à large échelle des ordinateurs, les rendant omniprésents. Aujourd’hui, le nombre d’objets connectés à Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent être des téléphones mobiles, des véhicules ou des senseurs, génèrent de très grands volumes de données qui sont presque toujours associés à un contexte spatiotemporel. Le volume de ces données est souvent si grand que leur traitement requiert la création de système distribués qui impliquent la coopération de plusieurs ordinateurs. La capacité de traiter ces données revêt une importance sociétale. Par exemple: les données collectées lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son véhicule. Un autre exemple: dans un avenir proche, les données collectées à l’aide de gyroscopes capables de détecter les trous dans la chaussée permettront de mieux planifier les interventions de maintenance à effectuer sur le réseau routier. Les domaines d’applications sont par conséquent nombreux, de même que les problèmes qui y sont associés. Les articles qui composent cette thèse traitent de systèmes qui partagent deux caractéristiques clés: un contexte spatiotemporel et une architecture décentralisée. De plus, les systèmes décrits dans ces articles s’articulent autours de trois axes temporels: le présent, le passé, et le futur. Les systèmes axés sur le présent permettent à un très grand nombre d’objets connectés de communiquer en fonction d’un contexte spatial avec des temps de réponses proche du temps réel. Nos contributions dans ce domaine permettent à ce type de système décentralisé de s’adapter au volume de donnée à traiter en s’étendant sur du matériel bon marché. Les systèmes axés sur le passé ont pour but de faciliter l’accès a de très grands volumes données spatiotemporelles collectées par des objets connectés. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particulièrement denses, ce qui n’avait pas été fait auparavant. Enfin, les systèmes axés sur le futur utilisent les trajectoires passées pour prédire les trajectoires que des objets connectés suivront dans l’avenir. Nos contributions permettent de prédire les trajectoires suivies par des objets connectés avec une granularité jusque là inégalée. Bien qu’impliquant des domaines différents, ces contributions s’articulent autour de dénominateurs communs des systèmes sous-jacents, ouvrant la possibilité de pouvoir traiter ces problèmes avec plus de généricité dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

Benchmarking real-time vehicle data streaming models for a smart city

The information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation service in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction.This research is partially supported by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (ERDF) through the “HERMES – SmartDriver” project (TIN2013-46801-C4-2-R), the “HERMES – Smart Citizen” project (TIN2013-46801-C4-1-R), and the “HERMES –Space&Time” project (TIN2013-46801-C4-3-R)

Prototyping a scalable Aggregate Computing cluster with open-source solutions

L'Internet of Things è un concetto che è stato ora adottato in modo pervasivo per descrivere un vasto insieme di dispositivi connessi attraverso Internet. Comunemente, i sistemi IoT vengono creati con un approccio bottom-up e si concentrano principalmente sul singolo dispositivo, il quale è visto come la basilare unità programmabile. Da questo metodo può emergere un comportamento comune trovato in molti sistemi esistenti che deriva dall'interazione di singoli dispositivi. Tuttavia, questo crea un'applicazione distribuita spesso dove i componenti sono strettamente legati tra di loro. Quando tali applicazioni crescono in complessità, tendono a soffrire di problemi di progettazione, mancanza di modularità e riusabilità, difficoltà di implementazione e problemi di test e manutenzione. L'Aggregate Programming fornisce un approccio top-down a questi sistemi, in cui l'unità di calcolo di base è un'aggregazione anziché un singolo dispositivo. Questa tesi consiste nella progettazione e nella distribuzione di una piattaforma, basata su tecnologie open-source, per supportare l'Aggregate Computing nel cloud, in cui i dispositivi saranno in grado di scegliere dinamicamente se il calcolo si trova su se stessi o nel cloud. Anche se Aggregate Computing è intrinsecamente progettato per un calcolo distribuito, il Cloud Computing introduce un'alternativa scalabile, affidabile e altamente disponibile come strategia di esecuzione. Quest'opera descrive come sfruttare una Reactive Platform per creare un'applicazione scalabile nel cloud. Dopo che la struttura, l'interazione e il comportamento dell'applicazione sono stati progettati, viene descritto come la distribuzione dei suoi componenti viene effettuata attraverso un approccio di containerizzazione con Kubernetes come orchestratore per gestire lo stato desiderato del sistema con una strategia di Continuous Delivery

Supporting Massive Mobility with stream processing software

The goal of this project is to design a solution for massive mobility using LISP protocol and scalable database systems like Apache Kafka. The project consists of three steps: rst, understanding the requirements of the massive mobility scenario; second, designing a solution based on a stream processing software that integrates with OOR (open-source LISP implementation). Third, building a prototype with OOR and a stream processing software (or a similar technology) and evaluating its performance. Our objectives are: Understand the requirements in an environment for massive mo- bility;Learn and evaluate the architecture of Apache Kafka and similar broker messages to see if these tools could satisfy the requirements; Propose an architecture for massive mobility using protocol LISP and Kafka as mapping system, and nally; Evaluate the performance of Apache Kafka using such architecture. In chapters 3 and 4 we will provide a summary of LISP protocol, Apache Kafka and other message brokers. On these chapters we describe the components of these tools and how we can use such components to achieve our objective. We will be evaluating the di erent mechanisms to 1) authenticate users, 2) access control list, 3) protocols to assure the delivery of the message, 4)integrity and 5)communication patterns. Because we are interested only in the last message of the queue, it is very important that the broker message provides a capability to obtain this message. Regarding the proposed architecture, we will see how we adapted Kafka to store the information managed by the mapping system in LISP. The EID in LISP will be repre- sented by topics in Apache Kafka., It will use the pattern publish-subscribe to spread the noti cation between all the subscribers. xTRs or Mobile devices will be able to play the role of Consumers and Publisher of the message brokers. Every topic will use only one partition and every subscriber will have its own consumer group to avoid competition to consume the messages. Finally we evaluate the performance of Apache Kafka. As we will see, Kafka escalates in a Linear way in the following cases: number of packets in the network in relation with the number of topics, number of packets in the network in relation with the number of subscribers, number of opened les by the server in relation with the number of topics time elapsed between the moment when publisher sends a message and subscriber receives it, regarding to the number of topics. In the conclusion we explain which objectives were achieved and why there are some challenges to be faced by kafka especially in two points: 1) we need only the last location (message) stored in the broker since Kafka does not provide an out of the box mechanism to obtain such messages, and 2) the amount of opened les that have to be managed simultaneously by the server. More study is required to compare the performance of Kafka against other tools

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case

An Experimental Platform for large-scale research facing FI-IoT scenarios

Providing experimental facilities for the Internet of Things (IoT) world is of paramount importance to materialise the Future Internet (FI) vision. The level of maturity achieved at the networking level in Sensor and Actuator networks (SAN) justifies the increasing demand on the research community to shift IoT testbed facilities from the network to the service and information management areas. In this paper we present an Experimental Platform fulfilling these needs by: integrating heterogeneous SAN infrastructures in a homogeneous way; providing mechanisms to handle information, and facilitating the development of experimental services. It has already been used to deploy applications in three different field trials: smart metering, smart places and environmental monitoring and it will be one of the components over which the SmartSantander project, that targets a large-scale IoT experimental facility, will rely o

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

A cloud architecture to integrate a multi-agent smart parking system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSmart parking systems are becoming a solution to recurring issues as the number of vehicles in traffic rises in major cities, which can be related to vehicle traffic congestion, unnecessary time spent searching for parking spots, and, consequently, environmental issues. The purpose of these systems is to help drivers who are searching for available parking spaces or who want to reserve for a specified period of time, quickly and, if possible, near the desired location. In this context, there are several modules within smart parking systems that may include cyber-physical systems, multi-agent systems, dynamic pricing and artificial intelligence. This dissertation presents the development of a smart parking system architecture, using Cloud-based technology to integrate a multiagent system into a scalable, decentralized, adaptable and safe environment. The proposed architecture was tested using, as a case study, a web system developed for the management and analysis of smart parking lots, as well as an application for mobile devices, which allows users to interact with multiple functionalities available in this system. Results obtained demonstrate that the implementation of the proposed architecture offers an efficient communication between users who use parking resources and the multi-agent system responsible for the autonomy and intelligence of the parking system.Os sistemas inteligentes de estacionamento estão se tornando uma solução para problemas recorrentes à medida que aumenta o número de veículos em trânsito nas grandes cidades, os quais podem estar relacionados ao congestionamento no tráfego de veículos, tempo desnecessário gasto na busca por vagas e, consequentemente, questões ambientais. O objetivo desses sistemas é auxiliar os motoristas que buscam por vagas disponíveis ou que desejam reservar por determinado período de tempo, de forma rápida e, se possível, próximo ao local desejado. Neste contexto, existem vários módulos dentro dos sistemas inteligentes de estacionamento que podem incluir sistemas ciberfísicos, sistemas multi-agentes, precificação dinâmica e inteligência artificial. Esta dissertação apresenta o desenvolvimento de uma arquitetura para sistemas inteligentes de estacionamento, utilizando tecnologia baseada em Nuvem para integrar um sistema multi-agentes em um ambiente escalável, descentralizado, adaptável e seguro. A arquitetura proposta foi testada utilizando, como um caso de estudo, um sistema web desenvolvido para gestão e análise de estacionamentos inteligentes, bem como uma aplicação para dispositivos móveis, que permite ao usuário interagir com as múltiplas funcionalidades disponibilizadas neste sistema. Resultados obtidos demonstram que a implementação da arquitetura proposta oferece uma comunicação eficiente entre usuários que utilizam os recursos e o sistema multi-agentes responsável pela autonomia e inteligência de um sistema de estacionamento