iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it’s targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.     

Optimal channel coordination in use-based product-service system contracts

Nowadays, service economy efficiencies are key to keep competitiveness and increase market advantages. The development of Product-Service Systems (PSS) can be an interesting strategy as they seek to improve the business performance of all the participants in the value chain. This paper proposes a novel reliability-based reward scheme for use-oriented PSS contracts. In a PSS instead of paying for the product, the client pays a fee for its performance. The contribution of this work to the existing literature is to provide a quantitative tool for the development of a use-oriented PSS contract based on non-repairable component reliability and risk sharing. It can be extended to other performance metrics such as availability and reliability. A well designed PSS achieves a mutual growth agreement for the client and the supplier if it aligns their interests through channel coordination. This is achieved by balancing the improvement in the expected cost and profit for the client and the supplier, respectively. The improvement is measured with respect to a baseline scenario where no PSS contract exists. The methodology is tested using a case study that analyses mining haul truck tires. The results show a significant overall improvement in the main key performance indicators and environmental impact of the value chain.     

Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers

Different solutions for the integration of high-speed rail (HSR) and air transport could be implemented, ranging from very basic integration to more sophisticated systems that include ticket and handling integration. This paper uses two statistical techniques, name cluster analysis and discrete choice models in order to investigate how different market segments have a determining influence in being more proactive to change to HSR for the second leg in multimodal trips. A discrete choice experiment is conducted to better understand passengers’ preferences. We obtain a number of clusters and estimate flexible choice models, taking into account the panel nature of stated preference data. We obtain a range of willingness-to-pay values for service quality attributes, finding results that can be used to infer policy conclusions about the real attractiveness of the Air-HSR integrated alternative. In this respect, clusters and schedule coordination, which reduces connecting time, are crucial to explain HSR attractiveness.     

An information based feedback control for audio-motor binaural localization

In static scenarios, binaural sound localization is fundamentally limited by front-back ambiguity and distance non-observability. Over the past few years, “active” schemes have been shown to overcome these shortcomings, by combining spatial binaural cues with the motor commands of the sensor. In this context, given a Gaussian prior on the relative position to a source, this paper determines an admissible motion of a binaural head which leads, on average, to the one-step-ahead most informative audio-motor localization. To this aim, a constrained optimization problem is set up, which consists in maximizing the entropy of the next predicted measurement probability density function over a cylindric admissible set. The method is appraised through geometrical arguments, and validated in simulations and on real-life robotic experiments.     

Modelling and analysis of bacterial tracks suggest an active reorientation mechanism in Rhodobacter sphaeroides

Most free-swimming bacteria move in approximately straight lines, interspersed with random reorientation phases. A key open question concerns varying mechanisms by which reorientation occurs. We combine mathematical modelling with analysis of a large tracking dataset to study the poorly understood reorientation mechanism in the monoflagellate species Rhodobacter sphaeroides. The flagellum on this species rotates counterclockwise to propel the bacterium, periodically ceasing rotation to enable reorientation. When rotation restarts the cell body usually points in a new direction. It has been assumed that the new direction is simply the result of Brownian rotation. We consider three variants of a self-propelled particle model of bacterial motility. The first considers rotational diffusion only, corresponding to a non-chemotactic mutant strain. Two further models incorporate stochastic reorientations, describing ‘run-and-tumble’ motility. We derive expressions for key summary statistics and simulate each model using a stochastic computational algorithm. We also discuss the effect of cell geometry on rotational diffusion. Working with a previously published tracking dataset, we compare predictions of the models with data on individual stopping events in R. sphaeroides. This provides strong evidence that this species undergoes some form of active reorientation rather than simple reorientation by Brownian rotation.     

Symbolic Supervisory Control of Infinite Transition Systems Under Partial Observation Using Abstract Interpretation

We propose algorithms for the synthesis of state-feedback controllers with partial observation of infinite state discrete event systems modelled by Symbolic Transition Systems. We provide models of safe memoryless controllers both for potentially deadlocking and deadlock free controlled systems. The termination of the algorithms solving these problems is ensured using abstract interpretation techniques which provide an overapproximation of the transitions to disable. We then extend our algorithms to controllers with memory and to online controllers. We also propose improvements in the synthesis of controllers in the finite case which, to our knowledge, provide more permissive solutions than what was previously proposed in the literature. Our tool SMACS gives an empirical validation of our methods by showing their feasibility, usability and efficiency.