Many competing designs for Distributed Hash Tables exist exploring multiple models of addressing, routing and network maintenance. Designing a general theoretical model and implementation of a Distributed Hash Table allows exploration of the possible properties of Distributed Hash Tables. We will propose a generalized model of DHT behavior, centered on utilizing Delaunay triangulation in a given metric space to maintain the networks topology. We will show that utilizing this model we can produce network topologies that approximate existing DHT methods and provide a starting point for further exploration. We will use our generalized model of DHT construction to design and implement more efficient Distributed Hash Table protocols, and discuss the qualities of potential successors to existing DHT technologies.

Cette thèse propose une nouvelle architecture Peer-to-Peer pour l’établissement de sessions SIP (Session Initiation Protocol) sur les réseaux ad hoc. SIP est un protocole con¸cu à l’origine sur un modèle centralisé est n’est pas nativement adapté aux réseaux mobiles ad hoc (MANET) en raison de leurs caractéristiques inhérentes de mobilité. Nous avons ciblé nos études sur un mécanisme de lookup distribué Peer-to-Peer (P2P) tolérant aux fautes, même en cas de mobilité des noeuds du réseau. Cette thèse s’articule autour de quatre principales contributions: Nous introduisons le concept de Structured Mesh Overlay Network (SMON) : un overlay P2P sur MANET permettant d’effectuer des lookups de ressources rapides dans un environnement ad hoc. SMON utilise une architecture cross layer design basée sur une Distributed Hash Table (DHT) utilisant directement les informations de routage OLSR. Cette architecture cross layer permet d’optimiser les performances du réseau overlay lors d’un changement de topologie du réseau. La seconde contribution, SIPMON, est un overlay SIP sur réseau SMON. Sa particularité est d’utiliser un DHT pour distribuer les identifiants d’objet SIP dans le réseau overlay SMON. Les expérimentations menées prouvent que cette approche garantit une durée de découverte SIP constante et permet un établissement de session plus rapide entre deux usagers sur réseau ad hoc. SIPMON ne s’applique cependant qu’à un réseau MANET isolé. Notre troisième contribution SIPMON+ permet un interfonctionnement de plusieurs overlays SIPMON connectés à Internet. SIPMON+ unifie donc les overlays de réseau et permet de joindre un client SIP qu’il soit localisé sur un réseau ad hoc ou sur l’internet. De plus, SIPMON+ permet une continuité de service sans couture lors du passage entre un réseau MANET et un réseau d’infrastructure. Notre prototype a démontré que les performances de temps d’établissement d’appel SIPMON+ étaient meilleures que pour l’approche concurrente MANEMO (MANET for Network Mobility). Le scénario d’usage principal est la fourniture de services de communication multimédia d’urgence rapidement déployables en cas de catastrophe majeure. Nous avons développé un prototype SIPMON+ totalement fonctionnel de service de communication P2P multimédia. Ce prototype a été expérimenté en situation réelle de catastrophe. Notre prototype sans infrastructure a donné de biens meilleurs résultats que MANEMO en termes de temps de déploiement, de taux de perte de paquets et de temps d’établissement d’appel. / This work presents a novel Peer to Peer (P2P) framework for Session Initiation Protocol (SIP) on Mobile Ad Hoc Network (MANET). SIP is a client-server model of computing which can introduce a single point of failure problem. P2P SIP addresses this problem by using a distributed implementation based on a P2P paradigm. However, both the traditional SIP and P2P SIP architectures are not suitable for MANETs because they are initially designed for infrastructured networks whose most nodes are static. We focus on distributed P2P resource lookup mechanisms for SIP which can tolerate failures resulting from the node mobility. Our target application is SIP-based multimedia communication in a rapidly deployable disaster emergency network. To achieve our goal, we provide four contributions as follows. The first contribution is a novel P2P lookup architecture based on a concept of P2P overlay network called a Structured Mesh Overlay Network (SMON). This overlay network enables P2P applications to perform fast resource lookups in the MANET environment. SMON utilizes a cross layer design based on the Distributed Hashing Table (DHT) and has direct access to OLSR routing information. Its cross layer design allows optimizing the overlay network performance during the change of network topology. The second contribution is a distributed SIP architecture on MANET providing SIP user location discovery in a P2P manner which tolerates single-point and multiple-point of failures. Our approach extends the traditional SIP user location discovery by utilizing DHT in SMON to distribute SIP object identifiers over SMON. It offers a constant time on SIP user discovery which results in a fast call setup time between two MANET users. From simulation and experiment results, we find that SIPMON provides the lowest call setup delay when compared to the existing broadcast-based approaches. The third contribution is an extended SIPMON supporting several participating MANETs connected to Internet. This extension (SIPMON+) provides seamless mobility support allowing a SIP user to roam from an ad hoc network to an infrastructured network such as Internet without interrupting an ongoing session. We propose a novel OLSR Overlay Network (OON), a single overlay network containing MANET nodes and some nodes on the Internet. These nodes can communicate using the same OLSR routing protocol. Therefore, SIPMON can be automatically extended without modifying SIPMON internal operations. Through our test-bed experiments, we prove that SIPMON+ has better performance in terms of call setup delay and handoff delay than MANET for Network Mobility (MANEMO). The fourth contribution is a proof-of-concept and a prototype of P2P multimedia communication based on SIPMON+ for post disaster recovery missions. We evaluate our prototype and MANEMO-based approaches through experimentation in real disaster situations (Vehicle to Infrastructure scenarios). We found that our prototype outperforms MANEMO-based approaches in terms of call setup delay, packet loss, and deployment time.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don Gruenbacher / Caterina Scoglio / In this dissertation, we investigate research gaps still existing in P2P file-sharing systems: the necessity of fairness maintenance during the content information publishing/retrieving process, and the stranger policies on P2P fairness. First, through a wide range of measurements in the KAD network, we present the impact of a poorly designed incentive fairness policy on the performance of looking up content information. The KAD network, designed to help peers publish and retrieve sharing information, adopts a distributed hash table (DHT) technology and combines itself into the aMule/eMule P2P file-sharing network. We develop a distributed measurement framework that employs multiple test nodes running on the PlanetLab testbed. During the measurements, the routing tables of around 20,000 peers are crawled and analyzed. More than 3,000,000 pieces of source location information from the publishing tables of multiple peers are retrieved and contacted. Based on these measurements, we show that the routing table is well maintained, while the maintenance policy for the source-location-information publishing table is not well designed. Both the current maintenance schedule for the publishing table and the poor incentive policy on publishing peers eventually result in the low availability of the publishing table, which accordingly cause low lookup performance of the KAD network. Moreover, we propose three possible solutions to address these issues: the self-maintenance scheme with short period renewal interval, the chunk-based publishing/retrieving scheme, and the fairness scheme. Second, using both numerical analyses and agent-based simulations, we evaluate the impact of different stranger policies on system performance and fairness. We explore that the extremely restricting stranger policy brings the best fairness at a cost of performance degradation. The varying tendency of performance and fairness under different stranger policies are not consistent. A trade-off exists between controlling free-riding and maintaining system performance. Thus, P2P designers are required to tackle strangers carefully according to their individual design goals. We also show that BitTorrent prefers to maintain fairness with an extremely restricting stranger policy, while aMule/eMule’s fully rewarding stranger policy promotes free-riders’ benefit.

This thesis presents a method to deploy and lookup applications and devices based on a geographical location. The proposed solution is a combination of two existing technologies, where the first one is a geocode system to encode latitude and longitude coordinates, and the second one is a Distributed Hash Table (DHT) where values are stored and accessed with a $<$key,value$>$ pair. The purpose of this work is to be able to search a specific location for the closest device that solves the user needs, such as finding an Internet of Things (IoT) device. The thesis covers a method for searching by iterating key-value pairs in the DHT and expanding the area to find the devices further away. The search is performed using two main algorithm implementations LayerExpand and SpiralBoxExpand, to scan the area around where the user started the search. LayerExpand and SpiralBoxExpand are tested and evaluated in comparison to each other. The comparison results are presented in the form of plots where both of the functions are shown together. The function analysis results show how the size of the DHT, the number of users, and size of the search area affects the performance of the searches.

Desde o início do século XXI, vivenciamos uma explosão na produção de informações de diversos tipos, tais como fotos, áudios, vídeos, entre outros. Dentre essas informações, existem aquelas em que a informação pode ser dividida em partes menores, mas que devem ser relacionadas seguindo uma ordem total. Um exemplo deste tipo de informação é um arquivo de vídeo que foi dividido em dez segmentos identificados com números de 1 a 10. Para reproduzir o vídeo original a partir dos segmentos é necessário que seus identificadores estejam ordenados. A estrutura denominada tabela de hash distribuída (DHT) tem sido amplamente utilizada para armazenar, atualizar e recuperar esse tipo de informação de forma eficiente em diversos cenários, como monitoramento de sensores e vídeo sob demanda. Entretanto, a DHT apresenta problemas de escalabilidade quando um membro da estrutura não consegue atender as requisições recebidas, trazendo como consequência a inacessibilidade da informação. Este trabalho apresenta uma arquitetura em camadas denominada MATe, que trata o problema da escalabilidade em dois níveis: estendendo a DHT com a introdução de agentes baseados na utilidade e organizando a quantidade de requisições solicitadas. A primeira camada trata a escalabilidade ao permitir a criação de novos agentes com o objetivo de distribuir as requisições evitando que um deles tenha a escalabilidade comprometida. A segunda camada é composta por grupos de dispositivos organizados de tal forma que somente alguns deles serão escolhidos para fazer requisições. A arquitetura foi implementada para dois cenários onde os problemas de escalabilidade acontecem: (i) monitoramento de sensores; e (ii) vídeo sob demanda. Para ambos cenários, os resultados experimentais mostraram que MATe melhora a escalabilidade quando comparada com as implementações originais da DHT. / Since the beginning of the 21st century, we have experienced an explosive growth in the generation of information, such as photos, audios, videos, among others. Within this information, there are some in which the information can be divided and related following a total order. For example, a video file can be divided into ten segments identified with numbers from 1 to 10. To play the original video from these segments, their identifiers must be fully ordered. A structure called Distributed Hash Table (DHT) has been widely used to efficiently store, update, and retrieve this kind of information in several application domains, such as video on demand and sensor monitoring. However, DHT encounters scalability issues when one of its members fails to answer the requests, resulting in information loss. This work presents MATe, a layered architecture that addresses the problem of scalability on two levels: extending the DHT with the introduction of utility-based agents and organizing the volume of requests. The first layer manages the scalability by allowing the creation of new agents to distribute the requests when one of them has compromised its scalability. The second layer is composed of groups of devices, organized in such a way that only a few of them will be chosen to perform requests. The architecture was implemented in two application scenarios where scalability problems arise: (i) sensor monitoring; and (ii) video on demand. For both scenarios, the experimental results show that MATe improves scalability when compared to original DHT implementations.

Large-scale graph-structured datasets are growing at an increasing rate. Social network graphs are an example of these datasets. Processing large-scale graphstructured datasets are central to many applications ranging from telecommunication to biology and has led to the development of many parallel graph algorithms. Performance of parallel graph algorithms largely depends on how the underlying graph is partitioned. In this work, we focus on studying streaming vertex-cut graph partitioning algorithms where partitioners receive a graph as a stream of vertices and edges and assign partitions to them on their arrival once and for all. Some of these algorithms maintain a state during partitioning. In some cases, the size of the state is so huge that it cannot be kept in a single machine memory. In many real world scenarios, several instances of a streaming graph partitioning algorithm are run simultaneously to improve the system throughput. However, running several instances of a partitioner drops the partitioning quality considerably due to the incomplete information of partitioners. Even frequently sharing states and its combination with buffering mechanisms does not completely solves the problem because of the heavy communication overhead produced by partitioners. In this thesis, we propose an algorithm which tackles the problem of low scalability and performance of existing streaming graph partitioning algorithms by providing an efficient way of sharing states and its combination with windowing mechanism. We compare state-of-the-art streaming graph partitioning algorithms with our proposed solution concerning performance and efficiency. Our solution combines a batch processing method with a shared-state mechanism to achieve both an outstanding performance and a high partitioning quality. Shared state mechanism is used for sharing states of partitioners. We provide a robust implementation of our method in a PowerGraph framework. Furthermore, we empirically evaluate the impact of partitioning quality on how graph algorithms perform in a real cloud environment. The results show that our proposed method outperforms other algorithms in terms of partitioning quality and resource consumption and improves partitioning time considerably. On average our method improves partitioning time by 23%, decreases communication load by 15% and increase memory consumption by only 5% compared to the state-of-the-art streaming graph partitioning.

An introduction to Chord Algorithm.

Source is converted into pdf format. An introduction to Chord Algorithm.

Desde o início do século XXI, vivenciamos uma explosão na produção de informações de diversos tipos, tais como fotos, áudios, vídeos, entre outros. Dentre essas informações, existem aquelas em que a informação pode ser dividida em partes menores, mas que devem ser relacionadas seguindo uma ordem total. Um exemplo deste tipo de informação é um arquivo de vídeo que foi dividido em dez segmentos identificados com números de 1 a 10. Para reproduzir o vídeo original a partir dos segmentos é necessário que seus identificadores estejam ordenados. A estrutura denominada tabela de hash distribuída (DHT) tem sido amplamente utilizada para armazenar, atualizar e recuperar esse tipo de informação de forma eficiente em diversos cenários, como monitoramento de sensores e vídeo sob demanda. Entretanto, a DHT apresenta problemas de escalabilidade quando um membro da estrutura não consegue atender as requisições recebidas, trazendo como consequência a inacessibilidade da informação. Este trabalho apresenta uma arquitetura em camadas denominada MATe, que trata o problema da escalabilidade em dois níveis: estendendo a DHT com a introdução de agentes baseados na utilidade e organizando a quantidade de requisições solicitadas. A primeira camada trata a escalabilidade ao permitir a criação de novos agentes com o objetivo de distribuir as requisições evitando que um deles tenha a escalabilidade comprometida. A segunda camada é composta por grupos de dispositivos organizados de tal forma que somente alguns deles serão escolhidos para fazer requisições. A arquitetura foi implementada para dois cenários onde os problemas de escalabilidade acontecem: (i) monitoramento de sensores; e (ii) vídeo sob demanda. Para ambos cenários, os resultados experimentais mostraram que MATe melhora a escalabilidade quando comparada com as implementações originais da DHT. / Since the beginning of the 21st century, we have experienced an explosive growth in the generation of information, such as photos, audios, videos, among others. Within this information, there are some in which the information can be divided and related following a total order. For example, a video file can be divided into ten segments identified with numbers from 1 to 10. To play the original video from these segments, their identifiers must be fully ordered. A structure called Distributed Hash Table (DHT) has been widely used to efficiently store, update, and retrieve this kind of information in several application domains, such as video on demand and sensor monitoring. However, DHT encounters scalability issues when one of its members fails to answer the requests, resulting in information loss. This work presents MATe, a layered architecture that addresses the problem of scalability on two levels: extending the DHT with the introduction of utility-based agents and organizing the volume of requests. The first layer manages the scalability by allowing the creation of new agents to distribute the requests when one of them has compromised its scalability. The second layer is composed of groups of devices, organized in such a way that only a few of them will be chosen to perform requests. The architecture was implemented in two application scenarios where scalability problems arise: (i) sensor monitoring; and (ii) video on demand. For both scenarios, the experimental results show that MATe improves scalability when compared to original DHT implementations.

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