2,671 research outputs found
Contention techniques for opportunistic communication in wireless mesh networks
Auf dem Gebiet der drahtlosen Kommunikation und insbesondere auf den tieferen Netzwerkschichten sind gewaltige Fortschritte zu verzeichnen. Innovative Konzepte und Technologien auf der physikalischen Schicht (PHY) gehen dabei zeitnah in zelluläre Netze ein. Drahtlose Maschennetzwerke (WMNs) können mit diesem Innovationstempo nicht mithalten. Die Mehrnutzer-Kommunikation ist ein Grundpfeiler vieler angewandter PHY Technologien, die sich in WMNs nur ungenügend auf die etablierte Schichtenarchitektur abbilden lässt. Insbesondere ist das Problem des Scheduling in WMNs inhärent komplex. Erstaunlicherweise ist der Mehrfachzugriff mit Trägerprüfung (CSMA) in WMNs asymptotisch optimal obwohl das Verfahren eine geringe Durchführungskomplexität aufweist. Daher stellt sich die Frage, in welcher Weise das dem CSMA zugrunde liegende Konzept des konkurrierenden Wettbewerbs (engl. Contention) für die Integration innovativer PHY Technologien verwendet werden kann. Opportunistische Kommunikation ist eine Technik, die die inhärenten Besonderheiten des drahtlosen Kanals ausnutzt. In der vorliegenden Dissertation werden CSMA-basierte Protokolle für die opportunistische Kommunikation in WMNs entwickelt und evaluiert. Es werden dabei opportunistisches Routing (OR) im zustandslosen Kanal und opportunistisches Scheduling (OS) im zustandsbehafteten Kanal betrachtet. Ziel ist es, den Durchsatz von elastischen Paketflüssen gerecht zu maximieren. Es werden Modelle für Überlastkontrolle, Routing und konkurrenzbasierte opportunistische Kommunikation vorgestellt. Am Beispiel von IEEE 802.11 wird illustriert, wie der schichtübergreifende Entwurf in einem Netzwerksimulator prototypisch implementiert werden kann. Auf Grundlage der Evaluationsresultate kann der Schluss gezogen werden, dass die opportunistische Kommunikation konkurrenzbasiert realisierbar ist. Darüber hinaus steigern die vorgestellten Protokolle den Durchsatz im Vergleich zu etablierten Lösungen wie etwa DCF, DSR, ExOR, RBAR und ETT.In the field of wireless communication, a tremendous progress can be observed especially at the lower layers. Innovative physical layer (PHY) concepts and technologies can be rapidly assimilated in cellular networks. Wireless mesh networks (WMNs), on the other hand, cannot keep up with the speed of innovation at the PHY due to their flat and decentralized architecture. Many innovative PHY technologies rely on multi-user communication, so that the established abstraction of the network stack does not work well for WMNs. The scheduling problem in WMNs is inherent complex. Surprisingly, carrier sense multiple access (CSMA) in WMNs is asymptotically utility-optimal even though it has a low computational complexity and does not involve message exchange. Hence, the question arises whether CSMA and the underlying concept of contention allows for the assimilation of advanced PHY technologies into WMNs. In this thesis, we design and evaluate contention protocols based on CSMA for opportunistic communication in WMNs. Opportunistic communication is a technique that relies on multi-user diversity in order to exploit the inherent characteristics of the wireless channel. In particular, we consider opportunistic routing (OR) and opportunistic scheduling (OS) in memoryless and slow fading channels, respectively. We present models for congestion control, routing and contention-based opportunistic communication in WMNs in order to maximize both throughput and fairness of elastic unicast traffic flows. At the instance of IEEE 802.11, we illustrate how the cross-layer algorithms can be implemented within a network simulator prototype. Our evaluation results lead to the conclusion that contention-based opportunistic communication is feasible. Furthermore, the proposed protocols increase both throughput and fairness in comparison to state-of-the-art approaches like DCF, DSR, ExOR, RBAR and ETT
Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks
The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks
Revisiting Actor Programming in C++
The actor model of computation has gained significant popularity over the
last decade. Its high level of abstraction makes it appealing for concurrent
applications in parallel and distributed systems. However, designing a
real-world actor framework that subsumes full scalability, strong reliability,
and high resource efficiency requires many conceptual and algorithmic additives
to the original model.
In this paper, we report on designing and building CAF, the "C++ Actor
Framework". CAF targets at providing a concurrent and distributed native
environment for scaling up to very large, high-performance applications, and
equally well down to small constrained systems. We present the key
specifications and design concepts---in particular a message-transparent
architecture, type-safe message interfaces, and pattern matching
facilities---that make native actors a viable approach for many robust,
elastic, and highly distributed developments. We demonstrate the feasibility of
CAF in three scenarios: first for elastic, upscaling environments, second for
including heterogeneous hardware like GPGPUs, and third for distributed runtime
systems. Extensive performance evaluations indicate ideal runtime behaviour for
up to 64 cores at very low memory footprint, or in the presence of GPUs. In
these tests, CAF continuously outperforms the competing actor environments
Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page
Contributions to the development of an integrated toolbox of solvers in Derivative-Free Optimization
This dissertation is framed on the ongoing research project BoostDFO - Improving
the performance and moving to newer dimensions in Derivative-Free Optimization. The final
goal of this project is to develop efficient and robust algorithms for Global and/or
Multiobjective Derivative-free Optimization. This type of optimization is typically required
in complex scientific/industrial applications, where the function evaluation is
time-consuming and derivatives are not available for use, neither can be numerically
approximated. Often problems present several conflicting objectives or users aspire to
obtain global solutions.
Inspired by successful approaches used in single objective local Derivative-free Optimization,
we intend to address the inherent problem of the huge execution times by
resorting to parallel/cloud computing and carrying a detailed performance analysis. As
result, an integrated toolbox for solving single/multi objective, local/global Derivativefree
Optimization problems is made available, with recommendations for taking advantage
of parallelization and cloud computing, providing easy access to several efficient and
robust algorithms and allowing to tackle harder Derivative-free Optimization problems.Esta dissertação insere-se no projecto científico BoostDFO - Improving the performance
and moving to newer dimensions in Derivative-Free Optimization. O objectivo final desta
investigação é desenvolver algoritmos robustos e eficientes para problemas de Optimização
Sem Derivadas Globais e/ou Multiobjectivo. Este tipo de optimização é tipicamente
requerido em aplicações científicas/industriais complexas, onde a avaliação da função é
bastante demorada e as derivadas não se encontram disponíveis, nem podem ser aproximadas
numericamente. Os problemas apresentam frequentemente vários objectivos
divergentes ou os utilizadores procuram obter soluções globais.
Tendo por base abordagens prévias bem-sucedidas utilizadas em Optimização Sem
Derivadas local e uniobjectivo, pretende-se abordar o problema inerente aos grandes tempos
de execução, recorrendo ao paralelismo/computação em cloud e efectuando uma
detalhada análise de desempenho. Como resultado, é disponibilizada uma ferramenta
integrada destinada a problemas de Optimização Sem Derivadas uni/multiobjectivo, com
optimização local/global, incluindo recomendações que permitam tirar partido do paralelismo
e computação em cloud, facilitando o acesso a vários algoritmos robustos e eficientes
e permitindo abordar problemas mais difíceis nesta classe
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