Cloud Workload Prediction by Means of Simulations

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application

Towards efficient virtual appliance delivery with minimal manageable virtual appliances

Infrastructure as a Service systems use virtual appliances to initiate virtual machines. As virtual appliances encapsulate applications and services with their support environment, their delivery is the most expensive task of the virtual machine creation. Virtual appliance delivery is a well-discussed topic in the field of cloud computing. However, for high efficiency, current techniques require the modification of the underlying IaaS systems. To target the wider adoptability of these delivery solutions, this article proposes the concept of minimal manageable virtual appliances (MMVA) that are capable of updating and configuring their virtual machines without the need to modify IaaS systems. To create MMVAs, we propose to reduce manageable virtual appliances until they become MMVAs. This research also reveals a methodology for appliance developers to incorporate MMVAs in their own appliances to enable their efficient delivery and wider adoptability. Finally, the article evaluates the positive effects of MMVAs on an already existing delivery solution: the Automated Virtual appliance creation Service (AVS). Through experimental evaluation, we present that the application of MMVAs not only increases the adoptability of a delivery solution but it also significantly improves its performance in highly dynamic systems. © 2013 IEEE

Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning

Learning complex structures from stimuli requires extended exposure and often repeated observation of the same stimuli. Learning induces stimulus-dependent changes in specific performance measures. The same performance measures, however, can also be affected by processes that arise due to extended training (e.g. fatigue) but are otherwise independent from learning. Thus, a thorough assessment of the properties of learning can only be achieved by identifying and accounting for the effects of such processes. Reactive inhibition is a process that modulates behavioral performance measures on a wide range of time scales and often has opposite effects than learning. Here we develop a tool to disentangle the effects of reactive inhibition from learning in the context of an implicit learning task, the alternating serial reaction time task. Our method highlights that the magnitude of the effect of reactive inhibition on measured performance is larger than that of the acquisition of statistical structure from stimuli. We show that the effect of reactive inhibition can be identified not only in population measures but also at the level of performance of individuals, revealing varying degrees of contribution of reactive inhibition. Finally, we demonstrate that a higher proportion of behavioral variance can be explained by learning once the effects of reactive inhibition are eliminated. These results demonstrate that reactive inhibition has a fundamental effect on the behavioral performance that can be identified in individual participants and can be separated from other cognitive processes like learning

Facilitating self-adaptable inter-cloud management

Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructures by hiding their proprietary solutions. As the infrastructure becomes more complex underneath these systems, the situations (like system failures, handling of load peaks and slopes) that users cannot easily handle, occur more and more frequently. Therefore, federations need to manage these situations autonomously without user interactions. This paper introduces a methodology to autonomously operate cloud federations by controlling their behavior with the help of knowledge management systems. Such systems do not only suggest reactive actions to comply with established Service Level Agreements (SLA) between provider and consumer, but they also find a balance between the fulfillment of established SLAs and resource consumption. The paper adopts rule-based techniques as its knowledge management solution and provides an extensible rule set for federated clouds built on top of multiple infrastructures. © 2012 IEEE

Stochastic Simulation of Mudcrack Damage Formation in an Environmental Barrier Coating

The FEAMAC/CARES program, which integrates finite element analysis (FEA) with the MAC/GMC (Micromechanics Analysis Code with Generalized Method of Cells) and the CARES/Life (Ceramics Analysis and Reliability Evaluation of Structures / Life Prediction) programs, was used to simulate the formation of mudcracks during the cooling of a multilayered environmental barrier coating (EBC) deposited on a silicon carbide substrate. FEAMAC/CARES combines the MAC/GMC multiscale micromechanics analysis capability (primarily developed for composite materials) with the CARES/Life probabilistic multiaxial failure criteria (developed for brittle ceramic materials) and Abaqus (Dassault Systmes) FEA. In this report, elastic modulus reduction of randomly damaged finite elements was used to represent discrete cracking events. The use of many small-sized low-aspect-ratio elements enabled the formation of crack boundaries, leading to development of mudcrack-patterned damage. Finite element models of a disk-shaped three-dimensional specimen and a twodimensional model of a through-the-thickness cross section subjected to progressive cooling from 1,300 C to an ambient temperature of 23 C were made. Mudcrack damage in the coating resulted from the buildup of residual tensile stresses between the individual material constituents because of thermal expansion mismatches between coating layers and the substrate. A two-parameter Weibull distribution characterized the coating layer stochastic strength response and allowed the effect of the Weibull modulus on the formation of damage and crack segmentation lengths to be studied. The spontaneous initiation of cracking and crack coalescence resulted in progressively smaller mudcrack cells as cooling progressed, consistent with a fractal-behaved fracture pattern. Other failure modes such as delamination, and possibly spallation, could also be reproduced. The physical basis assumed and the heuristic approach employed, which involves a simple stochastic cellular automaton methodology to approximate the crack growth process, are described. The results ultimately show that a selforganizing mudcrack formation can derive from a Weibull distribution that is used to describe the stochastic strength response of the bulk brittle ceramic material layers of an EBC

Evaluation of presence and concentration of ppv in rootstocks derived from prunus davidiana (carr.) Franch

Evaluation of the presence and concentration of PPV (Plum pox virus) in selected rootstocks was carried out in 2016-2017. For the purpose of the experiment we used rootstocks derived from crossbreeding of Prunus davidiana (Carr.) Franch, such as Cadaman and Barrier, and also a P. davidiana seedling. Peach seedling rootstock GF-305 was used as a control. The rootstocks were inoculated artificially with PPV strain M (Marcus). Both the rootstock and the inoculum were tested for presence of the virus by a serological method - semiquantitative DAS-ELISA test and molecular methods - RT-PCR, real-time RT-PCR and RT-LAMP. During the growing season the plants were evaluated for symptom intensity by using a scoring scale. The results show interdependency between symptom intensity and the amount of PPV in leaves, with DAS-ELISA test giving less positive samples than RT-PCR. The RT-LAMP and real-time RT-PCR methods were capable of revealing low concentrations of the virus even in symptom-free plants. The lowest PPV concentrations of all the four rootstocks were detected by real-time RT-PCR in P. davidiana. The highest PPV concentrations were detected in Barrier rootstock. In inocula, the lowest concentration was found in the inocula on Cadaman rootstock, whereas the highest PPV concentration was detected in the inocula inoculated on Barrier rootstock.O

One click cloud orchestrator: Bringing complex applications effortlessly to the clouds

© Springer International Publishing Switzerland 2014.Infrastructure cloud systems offer basic functionalities only for managing complex virtual infrastructures. These functionalities demand low-level understanding of applications and their infrastructural needs. Recent research has identified several techniques aimed at enabling the semi-automated management and using applications that span across multiple virtual machines. Even with these efforts however, a truly flexible and end-user oriented approach is missing. This paper presents the One Click Cloud Orchestrator that not only allows higher level of automated infrastructure management than it was possible before, but it also allows end-users to focus on their computational problems instead of the complex cloud infrastructures needed for their execution. To accomplish these goals the paper reveals the novel building blocks of our new orchestrator from the components closely related to infrastructure cloud to the ways virtual infrastructures are modeled. Finally, we show our initial evaluation and study on how the orchestrator fulfills the high level requirements of end-users

Gold-Silver Catalysts: Ruling Factors for Establishing Synergism

DPU and SOL immobilisation have been used to prepare 1 %AuAg/TiO2 with internal ratio 1 : 1 and 4 : 1 which have been studied as fresh, calcined in air at 300 \ub0C and reduced at 550 \ub0C in H2. TEM-EDS, XPS, UV-Vis and CO-DRIFT allowed to characterize the samples in terms of particle size, particle composition, exposure and oxidation state of metals. Correlating these characteristics to the catalytic behaviour we concluded that only Au-rich catalysts show synergistic effect, silver in bimetallic systems appears more resistant to oxidation than in monometallic one, thermal treatment enhances the SMSI thus producing (regardless to the post-treatment) almost the same amount of Au\u3b4+ and also Ag\u3b4+. Catalysts prepared by DPU (calcined in air or reduced in H2) are more active than SOL (fresh or calcined) probably due to the higher presence of gold at the surface

Advanced prevention against icing on high voltage power lines

Historical meteorological data indicates, that our weather is becoming more and more extreme. For the electrical utility operators (Distribution System Operators - DSOs and Transmission System Operators - TSOs), these changes arise in new operation challenges that need to be addressed. For example, frequent icing phenomenon affects all the components of the power line by a significant mechanical overload: it endangers the conductors, the insulators and the towers, as well. The result is often fatal and beside serious failures, it effects on operators’ decisions. These not only endanger the reliability of electrical grids by the loss of a power line for weeks or even months, but in general, the safety in the surroundings of the power line. As technology advances, we will be able to collected, analyses and predict very large databases in the field of meteorology and electrical engineering. The ability of processing mentioned data, combined with know-how results in the capacity to operate power lines at their thermal limits during different ambient parameters. This technology called Dynamic Line Rating (DLR) – is not only a great way to increase the transmission capacity of a given line, but can also be effectively used to prevent, or even solve icing-related issues. Higher currents result in higher Joule-heats, that consequently heat the conductors. If limits can be reached or approached, icing can be prevented. If prevention is not possible, detection and removal of ice layer is necessary. The proper handling of this icing issues, requires advanced algorithms (expert systems) and reliable measuring equipment. The combination and synchronization between algorithms, weather service and measuring equipment is the key of the successful operation. An EU H2020 financed project called FLEXITRANSTORE has just been launched to develop a cross-country co-operation, with objective to improve anti-icing and de-icing solutions. To establish and analyse different solutions, the project includes several universities, TSOs and DSOs. To solve mentioned icing issues Budapest University of Technology and Economics’ (BME) developed an advanced neural-network based algorithm which use OTLM system. It is planned to install and demonstrate the capabilities of this new technology on the DSOs grid (Electro Ljubljana - ELJ). Besides the introduction of DLR and icing, this paper also focuses on the preparation/organisation of co-operation between different companies and universities