Adaptivitätssensitive Platzierung von Replikaten in Adaptiven Content Distribution Networks

Adaptive Content Distribution Networks (A-CDNs) sind anwendungsübergreifende, verteilte Infrastrukturen, die auf Grundlage verteilter Replikation von Inhalten und Inhaltsadaption eine skalierbare Auslieferung von adaptierbaren multimedialen Inhalten an heterogene Clients ermöglichen. Die Platzierung der Replikate in den Surrogaten eines A-CDN wird durch den Platzierungsmechanismus des A-CDN gesteuert. Anders als in herkömmlichen CDNs, die keine Inhaltsadaption berücksichtigen, muss ein Platzierungsmechanismus in einem A-CDN nicht nur entscheiden, welches Inhaltsobjekt in welchem Surrogat repliziert werden soll, sondern darüber hinaus, in welcher Repräsentation bzw. in welchen Repräsentationen das Inhaltsobjekt zu replizieren ist. Herkömmliche Platzierungsmechanismen sind nicht in der Lage, verschiedene Repräsentationen eines Inhaltsobjektes zu berücksichtigen. Beim Einsatz herkömmlicher Platzierungsmechanismen in A-CDNs können deshalb entweder nur statisch voradaptierte Repräsentationen oder ausschließlich generische Repräsentationen repliziert werden. Während bei der Replikation von statisch voradaptierten Repräsentationen die Wiederverwendbarkeit der Replikate eingeschränkt ist, führt die Replikation der generischen Repräsentationen zu erhöhten Kosten und Verzögerungen für die dynamische Adaption der Inhalte bei jeder Anfrage. Deshalb werden in der Arbeit adaptivitätssensitive Platzierungsmechanismen zur Platzierung von Replikaten in A-CDNs vorgeschlagen. Durch die Berücksichtigung der Adaptierbarkeit der Inhalte bei der Ermittlung einer Platzierung von Replikaten in den Surrogaten des A-CDNs können adaptivitätssensitive Platzierungsmechanismen sowohl generische und statisch voradaptierte als auch teilweise adaptierte Repräsentationen replizieren. Somit sind sie in der Lage statische und dynamische Inhaltsadaption flexibel miteinander zu kombinieren. Das Ziel der vorliegenden Arbeit ist zu evaluieren, welche Vorteile sich durch die Berücksichtigung der Inhaltsadaption bei Platzierung von adaptierbaren Inhalten in A-CDNs realisieren lassen. Hierzu wird das Problem der adaptivitätssensitiven Platzierung von Replikaten in A-CDNs als Optimierungsproblem formalisiert, Algorithmen zur Lösung des Optimierungsproblems vorgeschlagen und diese in einem Simulator implementiert. Das zugrunde liegende Simulationsmodell beschreibt ein im Internet verteiltes A-CDN, welches zur Auslieferung von JPEG-Bildern an heterogene mobile und stationäre Clients verwendet wird. Anhand dieses Simulationsmodells wird die Leistungsfähigkeit der adaptivitätssensitiven Platzierungsmechanismen evaluiert und mit der von herkömmlichen Platzierungsmechanismen verglichen. Die Simulationen zeigen, dass der adaptivitätssensitive Ansatz in Abhängigkeit vom System- und Lastmodell sowie von der Speicherkapazität der Surrogate im A-CDN in vielen Fällen Vorteile gegenüber dem Einsatz herkömmlicher Platzierungsmechanismen mit sich bringt. Wenn sich die Anfragelasten verschiedener Typen von Clients jedoch nur wenig oder gar nicht überlappen oder bei hinreichend großer Speicherkapazität der Surrogate hat der adaptivitätssensitive Ansatz keine signifikanten Vorteile gegenüber dem Einsatz eines herkömmlichen Platzierungsmechanismus.Adaptive Content Distribution Networks (A-CDNs) are application independent, distributed infrastructures using content adaptation and distributed replication of contents to allow the scalable delivery of adaptable multimedia contents to heterogeneous clients. The replica placement in an A-CDN is controlled by the placement mechanisms of the A-CDN. As opposed to traditional CDNs, which do not take content adaptation into consideration, a replica placement mechanism in an A-CDN has to decide not only which object shall be stored in which surrogate but also which representation or which representations of the object to replicate. Traditional replica placement mechanisms are incapable of taking different representations of the same object into consideration. That is why A-CDNs that use traditional replica placement mechanisms may only replicate generic or statically adapted representations. The replication of statically adapted representations reduces the sharing of the replicas. The replication of generic representations results in adaptation costs and delays with every request. That is why the dissertation thesis proposes the application of adaptation-aware replica placement mechanisms. By taking the adaptability of the contents into account, adaptation-aware replica placement mechanisms may replicate generic, statically adapted and even partially adapted representations of an object. Thus, they are able to balance between static and dynamic content adaptation. The dissertation is targeted at the evaluation of the performance advantages of taking knowledge about the adaptability of contents into consideration when calculating a placement of replicas in an A-CDN. Therefore the problem of adaptation-aware replica placement is formalized as an optimization problem; algorithms for solving the optimization problem are proposed and implemented in a simulator. The underlying simulation model describes an Internet-wide distributed A-CDN that is used for the delivery of JPEG images to heterogeneous mobile and stationary clients. Based on the simulation model, the performance of the adaptation-aware replica placement mechanisms are evaluated and compared to traditional replica placement mechanisms. The simulations prove that the adaptation-aware approach is superior to the traditional replica placement mechanisms in many cases depending on the system and load model as well as the storage capacity of the surrogates of the A-CDN. However, if the load of different types of clients do hardly overlap or with sufficient storage capacity within the surrogates, the adaptation-aware approach has no significant advantages over the application of traditional replica-placement mechanisms

A Theory of Neural Computation with Clifford Algebras

The present thesis introduces Clifford Algebra as a framework for neural computation. Neural computation with Clifford algebras is model-based. This principle is established by constructing Clifford algebras from quadratic spaces. Then the subspace grading inherent to any Clifford algebra is introduced. The above features of Clifford algebras are then taken as motivation for introducing the Basic Clifford Neuron (BCN). As a second type of Clifford neuron the Spinor Clifford Neuron is presented. A systematic basis for Clifford neural computation is provided by the important notions of isomorphic Clifford neurons and isomorphic representations. After the neuron level is established, the discussion continues with (Spinor) Clifford Multilayer Perceptrons. First, (Spinor) Clifford Multilayer Perceptrons with real-valued activation functions ((S)CMLPs) are studied. A generic Backpropagation algorithm for CMLPs is derived. Also, universal approximation theorems for (S)CMLPs are presented. Finally, CMLPs with Clifford-valued activation functions are studied

Noise thermometry in narrow 2D electron gas heat baths connected to a quasi-1D interferometer

Thermal voltage noise measurements are performed in order to determine the electron temperature in nanopatterned channels of a GaAs/AlGaAs heterostructure at bath temperatures of 4.2 and 1.4 K. Two narrow two-dimensional (2D) heating channels, close to the transition to the one-dimensional (1D) regime, are connected by a quasi-1D quantum interferometer. Under dc current heating of the electrons in one heating channel, we perform cross-correlated noise measurements locally in the directly heated channel and nonlocally in the other channel, which is indirectly heated by hot electron diffusion across the quasi-1D connection. We observe the same functional dependence of the thermal noise on the heating current. The temperature dependence of the electron energy-loss rate is reduced compared to wider 2D systems. In the quantum interferometer, we show the decoherence due to the diffusion of hot electrons from the heating channel into the quasi-1D system, which causes a thermal gradient.Comment: 6 pages, 5 figure

Evaluation of Bacillus thuringiensis Berliner as an alternative control of small hive beetles, Aethina tumida Murray (Coleoptera: Nitidulidae)

Small hive beetles, Aethina tumida Murray, are parasites and scavengers of honeybee colonies, Apis mellifera L., and have become an invasive species that can cause considerable damage in its new distribution areas. An effective subspecies of Bacillus thuringiensis Berliner (=Bt) would provide an alternative to chemical control of this pest. Therefore, we tested three different Bt strains [B. thuringiensis, var. aizawai (B401®), B. thuringiensis var. kurstaki (Novodor®) and B. thuringiensis var. San Diego tenebrionis (Jackpot®)] and Perizin® (3.2% coumaphos), each applied on combs with a pollen diet fed to pairs of adult beetles. This evaluates the products for the suppression of successful small hive beetle reproduction. While none of the tested Bt strains showed a significant effect on the number of produced wandering larvae, we could confirm the efficacy of coumaphos for the control of small hive beetles. We further show that it is also efficient when applied with a lower concentration as a liquid on the combs. We suggest the continued search for efficient Bt strains naturally infesting small hive beetles in its endemic and new ranges, which may become a part of the integrated management of this pes