Stressed Web Environments as Strategic Games: Risk Profiles and Weltanschauung

We consider the behaviour of a set of services in a stressed web environment where performance patterns may be difficult to predict. In stressed environments the performances of some providers may degrade while the performances of others, with elastic resources, may improve. The allocation of web-based providers to users (brokering) is modelled by a strategic non-cooperative angel-daemon game with risk profiles. A risk profile specifies a bound on the number of unreliable service providers within an environment without identifying the names of these providers. Risk profiles offer a means of analysing the behaviour of broker agents which allocate service providers to users. A Nash equilibrium is a fixed point of such a game in which no user can locally improve their choice of provider – thus, a Nash equilibrium is a viable solution to the provider/user allocation problem. Angel daemon games provide a means of reasoning about stressed environments and offer the possibility of designing brokers using risk profiles and Nash equilibria.

High-order harmonic generation from controlled plasma mirrors

Ultrashort, high contrast laser pulses when focused to high intensity and reflected from a steep solid density 'plasma mirror (PM)' contain coherent XUV radiation in the form of high-order harmonics. The emission can either be due to the relativistically driven oscillating PM (ROM) [1] or due to Coherent wake emission (CWE) [2]. Selective control over the mechanisms and the characteristics of these harmonics and understanding the physics is crucial for the development of intense attosecond light sources. © 2013 IEEE.

Characterizing Aquifer Heterogeneity Using Bacterial and Bacteriophage Tracers

Gravel aquifers act as important potable water sources in central western Europe yet they are subject to numerous contamination pressures. Compositional and textural heterogeneity makes protection zone delineation around groundwater supplies in these units challenging; artificial tracer testing aids characterization. This paper reappraises previous tracer test results in light of new geological and microbiological data. Comparative passive gradient testing, using a fluorescent solute (Uranine), virus (H40/1 bacteriophage), and comparably sized bacterial tracers Escherichia coli and Pseudomonas putida, was used to investigate a calcareous gravel aquifer’s ability to remove microbiological contaminants at a test site near Munich, Germany. Test results revealed E. coli relative recoveries could exceed those of H40/1 at monitoring wells 10 m and 20 m from an injection well by almost four times; P. putida recoveries varied by a factor of up to three between wells. Application of filtration theory suggested greater attenuation of H40/1 relative to similarly charged E. coli occurred due to differences in microorganism size, while estimated collision efficiencies appeared comparable. By contrast, more positively charged P. putida experienced greater attenuation at one monitoring point, while lower attenuation rates at the second location indicated the influence of geochemical heterogeneity. Test findings proved consistent with observations from nearby fresh outcrops that suggested thin open framework gravel beds dominated mass transport in the aquifer, while discrete intervals containing stained clasts reflect localized geochemical heterogeneity. Study results highlight the utility of reconciling outcrop observations with artificial tracer test responses, using microbiological tracers with well-defined properties, to characterize aquifer heterogeneity.

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

How can GPU acceleration be obtained as a service in a cluster? This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), such that the nodes of a cluster can request the acceleration of a set of remote GPUs on demand. The rCUDA framework exploits virtualisation and ensures that multiple nodes can share the same GPU. In this paper we test the feasibility of the rCUDA framework on a real-world application employed in the financial risk industry that can benefit from AaaS in the production setting. The results confirm the feasibility of rCUDA and highlight that rCUDA achieves similar performance compared to CUDA, provides consistent results, and more importantly, allows for a single application to benefit from all the GPUs available in the cluster without loosing efficiency.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.