Lamred : location-aware and privacy preserving multi-layer resource discovery for IoT

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited computation power, the centralized approach leads to some efficiency and security issues. In this paper we proposed a location aware and decentralized multi layer model of resource discovery (LaMRD) in IoT. It allows a resource to be registered publicly or privately, and to be discovered in a decentralized scheme in the IoT network. LaMRD is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. Our proposed model utilizes Distributed Hash Table (DHT) technology to create a p2p scheme of communication among fog nodes. The resources are registered in LaMRD based on their locations which results in a low added overhead in the registration and discovery processes. LaMRD generates a single overlay and it can be generated without specific organizing entity or location based devices. LaMRD guarantees some important security properties and it showed a lower latency comparing to the cloud based and decentralized resource discovery

Engineering environment-mediated coordination via nature-inspired laws

SAPERE is a general multiagent framework to support the development of self-organizing pervasive computing services. One of the key aspects of the SAPERE approach is to have all interactions between agents take place in an indirect way, via a shared spatial environment. In such environment, a set of nature-inspired coordination laws have been defined to rule the coordination activities of the application agents and promote the provisioning of adaptive and self-organizing services

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

Data Storage and Dissemination in Pervasive Edge Computing Environments

Nowadays, smart mobile devices generate huge amounts of data in all sorts of gatherings. Much of that data has localized and ephemeral interest, but can be of great use if shared among co-located devices. However, mobile devices often experience poor connectivity, leading to availability issues if application storage and logic are fully delegated to a remote cloud infrastructure. In turn, the edge computing paradigm pushes computations and storage beyond the data center, closer to end-user devices where data is generated and consumed. Hence, enabling the execution of certain components of edge-enabled systems directly and cooperatively on edge devices. This thesis focuses on the design and evaluation of resilient and efficient data storage and dissemination solutions for pervasive edge computing environments, operating with or without access to the network infrastructure. In line with this dichotomy, our goal can be divided into two specific scenarios. The first one is related to the absence of network infrastructure and the provision of a transient data storage and dissemination system for networks of co-located mobile devices. The second one relates with the existence of network infrastructure access and the corresponding edge computing capabilities. First, the thesis presents time-aware reactive storage (TARS), a reactive data storage and dissemination model with intrinsic time-awareness, that exploits synergies between the storage substrate and the publish/subscribe paradigm, and allows queries within a specific time scope. Next, it describes in more detail: i) Thyme, a data storage and dis- semination system for wireless edge environments, implementing TARS; ii) Parsley, a flexible and resilient group-based distributed hash table with preemptive peer relocation and a dynamic data sharding mechanism; and iii) Thyme GardenBed, a framework for data storage and dissemination across multi-region edge networks, that makes use of both device-to-device and edge interactions. The developed solutions present low overheads, while providing adequate response times for interactive usage and low energy consumption, proving to be practical in a variety of situations. They also display good load balancing and fault tolerance properties.Resumo Hoje em dia, os dispositivos móveis inteligentes geram grandes quantidades de dados em todos os tipos de aglomerações de pessoas. Muitos desses dados têm interesse loca- lizado e efêmero, mas podem ser de grande utilidade se partilhados entre dispositivos co-localizados. No entanto, os dispositivos móveis muitas vezes experienciam fraca co- nectividade, levando a problemas de disponibilidade se o armazenamento e a lógica das aplicações forem totalmente delegados numa infraestrutura remota na nuvem. Por sua vez, o paradigma de computação na periferia da rede leva as computações e o armazena- mento para além dos centros de dados, para mais perto dos dispositivos dos utilizadores finais onde os dados são gerados e consumidos. Assim, permitindo a execução de certos componentes de sistemas direta e cooperativamente em dispositivos na periferia da rede. Esta tese foca-se no desenho e avaliação de soluções resilientes e eficientes para arma- zenamento e disseminação de dados em ambientes pervasivos de computação na periferia da rede, operando com ou sem acesso à infraestrutura de rede. Em linha com esta dico- tomia, o nosso objetivo pode ser dividido em dois cenários específicos. O primeiro está relacionado com a ausência de infraestrutura de rede e o fornecimento de um sistema efêmero de armazenamento e disseminação de dados para redes de dispositivos móveis co-localizados. O segundo diz respeito à existência de acesso à infraestrutura de rede e aos recursos de computação na periferia da rede correspondentes. Primeiramente, a tese apresenta armazenamento reativo ciente do tempo (ARCT), um modelo reativo de armazenamento e disseminação de dados com percepção intrínseca do tempo, que explora sinergias entre o substrato de armazenamento e o paradigma pu- blicação/subscrição, e permite consultas num escopo de tempo específico. De seguida, descreve em mais detalhe: i) Thyme, um sistema de armazenamento e disseminação de dados para ambientes sem fios na periferia da rede, que implementa ARCT; ii) Pars- ley, uma tabela de dispersão distribuída flexível e resiliente baseada em grupos, com realocação preventiva de nós e um mecanismo de particionamento dinâmico de dados; e iii) Thyme GardenBed, um sistema para armazenamento e disseminação de dados em redes multi-regionais na periferia da rede, que faz uso de interações entre dispositivos e com a periferia da rede. As soluções desenvolvidas apresentam baixos custos, proporcionando tempos de res- posta adequados para uso interativo e baixo consumo de energia, demonstrando serem práticas nas mais diversas situações. Estas soluções também exibem boas propriedades de balanceamento de carga e tolerância a faltas

Clouder : a flexible large scale decentralized object store

Programa Doutoral em Informática MAP-iLarge scale data stores have been initially introduced to support a few concrete extreme scale applications such as social networks. Their scalability and availability requirements often outweigh sacrificing richer data and processing models, and even elementary data consistency. In strong contrast with traditional relational databases (RDBMS), large scale data stores present very simple data models and APIs, lacking most of the established relational data management operations; and relax consistency guarantees, providing eventual consistency. With a number of alternatives now available and mature, there is an increasing willingness to use them in a wider and more diverse spectrum of applications, by skewing the current trade-off towards the needs of common business users, and easing the migration from current RDBMS. This is particularly so when used in the context of a Cloud solution such as in a Platform as a Service (PaaS). This thesis aims at reducing the gap between traditional RDBMS and large scale data stores, by seeking mechanisms to provide additional consistency guarantees and higher level data processing primitives in large scale data stores. The devised mechanisms should not hinder the scalability and dependability of large scale data stores. Regarding, higher level data processing primitives this thesis explores two complementary approaches: by extending data stores with additional operations such as general multi-item operations; and by coupling data stores with other existent processing facilities without hindering scalability. We address this challenges with a new architecture for large scale data stores, efficient multi item access for large scale data stores, and SQL processing atop large scale data stores. The novel architecture allows to find the right trade-offs among flexible usage, efficiency, and fault-tolerance. To efficient support multi item access we extend first generation large scale data store’s data models with tags and a multi-tuple data placement strategy, that allow to efficiently store and retrieve large sets of related data at once. For efficient SQL support atop scalable data stores we devise design modifications to existing relational SQL query engines, allowing them to be distributed. We demonstrate our approaches with running prototypes and extensive experimental evaluation using proper workloads.Os sistemas de armazenamento de dados de grande escala foram inicialmente desenvolvidos para suportar um leque restrito de aplicacões de escala extrema, como as redes sociais. Os requisitos de escalabilidade e elevada disponibilidade levaram a sacrificar modelos de dados e processamento enriquecidos e até a coerência dos dados. Em oposição aos tradicionais sistemas relacionais de gestão de bases de dados (SRGBD), os sistemas de armazenamento de dados de grande escala apresentam modelos de dados e APIs muito simples. Em particular, evidenciasse a ausência de muitas das conhecidas operacões de gestão de dados relacionais e o relaxamento das garantias de coerência, fornecendo coerência futura. Atualmente, com o número de alternativas disponíveis e maduras, existe o crescente interesse em usá-los num maior e diverso leque de aplicacões, orientando o atual compromisso para as necessidades dos típicos clientes empresariais e facilitando a migração a partir das atuais SRGBD. Isto é particularmente importante no contexto de soluções cloud como plataformas como um servic¸o (PaaS). Esta tese tem como objetivo reduzir a diferencça entre os tradicionais SRGDBs e os sistemas de armazenamento de dados de grande escala, procurando mecanismos que providenciem garantias de coerência mais fortes e primitivas com maior capacidade de processamento. Os mecanismos desenvolvidos não devem comprometer a escalabilidade e fiabilidade dos sistemas de armazenamento de dados de grande escala. No que diz respeito às primitivas com maior capacidade de processamento esta tese explora duas abordagens complementares : a extensão de sistemas de armazenamento de dados de grande escala com operacões genéricas de multi objeto e a junção dos sistemas de armazenamento de dados de grande escala com mecanismos existentes de processamento e interrogac¸ ˜ao de dados, sem colocar em causa a escalabilidade dos mesmos. Para isso apresent´amos uma nova arquitetura para os sistemas de armazenamento de dados de grande escala, acesso eficiente a m´ultiplos objetos, e processamento de SQL sobre sistemas de armazenamento de dados de grande escala. A nova arquitetura permite encontrar os compromissos adequados entre flexibilidade, eficiˆencia e tolerˆancia a faltas. De forma a suportar de forma eficiente o acesso a m´ultiplos objetos estendemos o modelo de dados de sistemas de armazenamento de dados de grande escala da primeira gerac¸ ˜ao com palavras-chave e definimos uma estrat´egia de colocac¸ ˜ao de dados para m´ultiplos objetos que permite de forma eficiente armazenar e obter grandes quantidades de dados de uma s´o vez. Para o suporte eficiente de SQL sobre sistemas de armazenamento de dados de grande escala, analisámos a arquitetura dos motores de interrogação de SRGBDs e fizemos alterações que permitem que sejam distribuídos. As abordagens propostas são demonstradas através de protótipos e uma avaliacão experimental exaustiva recorrendo a cargas adequadas baseadas em aplicações reais

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor