340 research outputs found
DĂ©couverte et allocation des ressources pour le traitement de requĂȘtes dans les systĂšmes grilles
De nos jours, les systĂšmes Grille, grĂące Ă leur importante capacitĂ© de calcul et de stockage ainsi que leur disponibilitĂ©, constituent l'un des plus intĂ©ressants environnements informatiques. Dans beaucoup de diffĂ©rents domaines, on constate l'utilisation frĂ©quente des facilitĂ©s que les environnements Grille procurent. Le traitement des requĂȘtes distribuĂ©es est l'un de ces domaines oĂč il existe de grandes activitĂ©s de recherche en cours, pour transfĂ©rer l'environnement sous-jacent des systĂšmes distribuĂ©s et parallĂšles Ă l'environnement Grille. Dans le cadre de cette thĂšse, nous nous concentrons sur la dĂ©couverte des ressources et des algorithmes d'allocation de ressources pour le traitement des requĂȘtes dans les environnements Grille. Pour ce faire, nous proposons un algorithme de dĂ©couverte des ressources pour le traitement des requĂȘtes dans les systĂšmes Grille en introduisant le contrĂŽle de topologie auto-stabilisant et l'algorithme de dĂ©couverte des ressources dirigĂ© par l'Ă©lection convergente. Ensuite, nous prĂ©sentons un algorithme d'allocation des ressources, qui rĂ©alise l'allocation des ressources pour les requĂȘtes d'opĂ©rateur de jointure simple par la gĂ©nĂ©ration d'un espace de recherche rĂ©duit pour les nĆuds candidats et en tenant compte des proximitĂ©s des candidats aux sources de donnĂ©es. Nous prĂ©sentons Ă©galement un autre algorithme d'allocation des ressources pour les requĂȘtes d'opĂ©rateurs de jointure multiple. Enfin, on propose un algorithme d'allocation de ressources, qui apporte une tolĂ©rance aux pannes lors de l'exĂ©cution de la requĂȘte par l'utilisation de la rĂ©plication passive d'opĂ©rateurs Ă Ă©tat. La contribution gĂ©nĂ©rale de cette thĂšse est double. PremiĂšrement, nous proposons un nouvel algorithme de dĂ©couverte de ressource en tenant compte des caractĂ©ristiques des environnements Grille. Nous nous adressons Ă©galement aux problĂšmes d'extensibilitĂ© et de dynamicitĂ© en construisant une topologie efficace sur l'environnement Grille et en utilisant le concept d'auto-stabilisation, et par la suite nous adressons le problĂšme de l'hĂ©tĂ©rogĂ©nĂ©itĂ© en proposant l'algorithme de dĂ©couverte de ressources dirigĂ© par l'Ă©lection convergente. La deuxiĂšme contribution de cette thĂšse est la proposition d'un nouvel algorithme d'allocation des ressources en tenant compte des caractĂ©ristiques de l'environnement Grille. Nous abordons les problĂšmes causĂ©s par la grande Ă©chelle caractĂ©ristique en rĂ©duisant l'espace de recherche pour les ressources candidats. De ce fait nous rĂ©duisons les coĂ»ts de communication au cours de l'exĂ©cution de la requĂȘte en allouant des nĆuds au plus prĂšs des sources de donnĂ©es. Et enfin nous traitons la dynamicitĂ© des nĆuds, du point de vue de leur existence dans le systĂšme, en proposant un algorithme d'affectation des ressources avec une tolĂ©rance aux pannes.Grid systems are today's one of the most interesting computing environments because of their large computing and storage capabilities and their availability. Many different domains profit the facilities of grid environments. Distributed query processing is one of these domains in which there exists large amounts of ongoing research to port the underlying environment from distributed and parallel systems to the grid environment. In this thesis, we focus on resource discovery and resource allocation algorithms for query processing in grid environments. For this, we propose resource discovery algorithm for query processing in grid systems by introducing self-stabilizing topology control and converge-cast based resource discovery algorithms. Then, we propose a resource allocation algorithm, which realizes allocation of resources for single join operator queries by generating a reduced search space for the candidate nodes and by considering proximities of candidates to the data sources. We also propose another resource allocation algorithm for queries with multiple join operators. Lastly, we propose a fault-tolerant resource allocation algorithm, which provides fault-tolerance during the execution of the query by the use of passive replication of stateful operators. The general contribution of this thesis is twofold. First, we propose a new resource discovery algorithm by considering the characteristics of the grid environments. We address scalability and dynamicity problems by constructing an efficient topology over the grid environment using the self-stabilization concept; and we deal with the heterogeneity problem by proposing the converge-cast based resource discovery algorithm. The second main contribution of this thesis is the proposition of a new resource allocation algorithm considering the characteristics of the grid environment. We tackle the scalability problem by reducing the search space for candidate resources. We decrease the communication costs during the query execution by allocating nodes closer to the data sources. And finally we deal with the dynamicity of nodes, in terms of their existence in the system, by proposing the fault-tolerant resource allocation algorithm
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Valuation of online social networks - An economic model and its application using the case of Xing.com
Ubiquitous information technologies like RFID allow for immediate, extensive and fine-grained
capture of real world information. Scalable and efficient networks for exchange of this vast amount of
information amongst companies are crucial for the economic exploitation of benefits of ubiquitous
information technologies. Existing networks bear several limitations like risks of single-point-offailures or bottlenecks, unequally distributed power and burdens as well as inflexibility through
stringent structures and formats. In particular there is a need for improving the scalability of solutions
and ensuring autonomy of network participants. In this paper we introduce a Peer-to-Peer-based
architecture for exchanging distributed information, which are shared among participants of a supply
chain facilitated with ubiquitous information technologies. This architecture builds on the wellestablished EPCglobal standards, but can be implemented as an autonomous network. Unlike other
architectures it does not need central coordination mechanisms, because it is based on self-organizing
Peer-to-Peer protocols. We argue that our architecture supports business processes especially of
small and medium-sized enterprises better than other architectures. We provide a discussion about
requirements for solutions and a simulation-based analysis of the proposed architecture
Resource Management in a Peer to Peer Cloud Network for IoT
Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale of data centers, heterogeneity issues and their interconnections have made the resource management a hard problem specially when there are different actors in cloud system with different needs. Resource management is a vital requirement to achieve robust networks specially with facing continuously increasing amount of heterogeneous resources and devices to the network. The goal of this paper is reviews to address IoT resource management issues in cloud computing services. We discuss the bottlenecks of cloud networks for IoT services such as mobility. We review Fog computing in IoT services to solve some of these issues. It provides a comprehensive literature review of around one hundred studies on resource management in Peer to Peer Cloud Networks and IoT. It is very important to find a robust design to efficiently manage and provision requests and available resources. We also reviewed different search methodologies to help clients find proper resources to answer their needs
Dragon: Multidimensional Range Queries on Distributed Aggregation Trees,
Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of
Things (IoT) and cyber-physical systems. Even if several multi-attribute range queries supports have been proposed for
peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms
for IoT, like fog computing. This paper proposes dragon, an ecient support for distributed multi-dimensional range
query processing targeting ecient query resolution on highly dynamic data. In dragon nodes at the edges of the
network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data
digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches.
Multi-attribute queries are managed by linearising the attribute space through space lling curves. We extensively
analysed dierent aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that
dragon manages eciently fast changing data values. Further, we show that dragon resolves queries by contacting a
lower number of nodes when compared to a similar approach in the state of the art
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