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Cost models play an important role for the efficient implementation of software systems. These models can be embedded in operating systems and execution environments to optimize execution at run time. Even though non-uniform memory access (NUMA) architectures are dominating today's server landscape, there is still a lack of parallel cost models that represent NUMA system sufficiently. Therefore, the existing NUMA models are analyzed, and a two-step performance assessment strategy is proposed that incorporates low-level hardware counters as performance indicators. To support the two-step strategy, multiple tools are developed, all accumulating and enriching specific hardware event counter information, to explore, measure, and visualize these low-overhead performance indicators. The tools are showcased and discussed alongside specific experiments in the realm of performance assessment.
This paper discusses a new approach for designing and deploying Security-as-a-Service (SecaaS) applications using cloud native design patterns. Current SecaaS approaches do not efficiently handle the increasing threats to computer systems and applications. For example, requests for security assessments drastically increase after a high-risk security vulnerability is disclosed. In such scenarios, SecaaS applications are unable to dynamically scale to serve requests. A root cause of this challenge is employment of architectures not specifically fitted to cloud environments. Cloud native design patterns resolve this challenge by enabling certain properties e.g. massive scalability and resiliency via the combination of microservice patterns and cloud-focused design patterns. However adopting these patterns is a complex process, during which several security issues are introduced. In this work, we investigate these security issues, we redesign and deploy a monolithic SecaaS application using cloud native design patterns while considering appropriate, layered security counter-measures i.e. at the application and cloud networking layer. Our prototype implementation out-performs traditional, monolithic applications with an average Scanner Time of 6 minutes, without compromising security. Our approach can be employed for designing secure, scalable and performant SecaaS applications that effectively handle unexpected increase in security assessment requests.
Mixed-projection treemaps
(2017)
This paper presents a novel technique for combining 2D and 2.5D treemaps using multi-perspective views to leverage the advantages of both treemap types. It enables a new form of overview+detail visualization for tree-structured data and contributes new concepts for real-time rendering of and interaction with treemaps. The technique operates by tilting the graphical elements representing inner nodes using affine transformations and animated state transitions. We explain how to mix orthogonal and perspective projections within a single treemap. Finally, we show application examples that benefit from the reduced interaction overhead.
The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.
The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made.
Lately, first implementation approaches of Internet of Things (IoT) technologies penetrate industrial value-adding processes. Within this, the competence requirements for employees are changing. Employees’ organization, process, and interaction competences are of crucial importance in this new IoT environment, however, in students and vocational training not sufficiently considered yet. On the other hand, conventional learning factories evolve and transform to digital learning factories. Nevertheless, the integration of IoT technology and its usage for training in digital learning factories has been largely neglected thus far. Existing learning factories do not explicitly and properly consider IoT technology, which leads to deficiencies regarding an appropriate development of employees’ Industrial IoT competences. The goal of this contribution is to point out a didactic concept that enables development and training of these new demanded competences by using an IoT laboratory. For this purpose, a design science approach is applied. The result of this contribution is a didactic concept for the development of Industrial IoT competences in an IoT laboratory.
In this extended abstract, we will analyze the current challenges for the envisioned Self-Adaptive CPS. In addition, we will outline our results to approach these challenges with SMARTSOS [10] a generic approach based on extensions of graph transformation systems employing open and adaptive collaborations and models at runtime for trustworthy self-adaptation, self-organization, and evolution of the individual systems and the system-of-systems level taking the independent development, operation, management, and evolution of these systems into account.
E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.
We compare Visual Berrypicking, an interactive approach allowing users to explore large and highly faceted information spaces using similarity-based two-dimensional maps, with traditional browsing techniques. For large datasets, current projection methods used to generate maplike overviews suffer from increased computational costs and a loss of accuracy resulting in inconsistent visualizations. We propose to interactively align inexpensive small maps, showing local neighborhoods only, which ideally creates the impression of panning a large map. For evaluation, we designed a web-based prototype for movie exploration and compared it to the web interface of The Movie Database (TMDb) in an online user study. Results suggest that users are able to effectively explore large movie collections by hopping from one neighborhood to the next. Additionally, due to the projection of movie similarities, interesting links between movies can be found more easily, and thus, compared to browsing serendipitous discoveries are more likely.
We develop a simple two-zone interpretation of the broadband baseline Crab nebula spectrum between 10(-5) eV and similar to 100 TeV by using two distinct log-parabola energetic electrons distributions. We determine analytically the very-high energy photon spectrum as originated by inverse-Compton scattering of the far-infrared soft ambient photons within the nebula off a first population of electrons energized at the nebula termination shock. The broad and flat 200 GeV peak jointly observed by Fermi/LAT and MAGIC is naturally reproduced. The synchrotron radiation from a second energetic electron population explains the spectrum from the radio range up to similar to 10 keV. We infer from observations the energy dependence of the microscopic probability of remaining in proximity of the shock of the accelerating electrons.
Web-based E-Learning uses Internet technologies and digital media to deliver education content to learners. Many universities in recent years apply their capacity in producing Massive Open Online Courses (MOOCs). They have been offering MOOCs with an expectation of rendering a comprehensive online apprenticeship. Typically, an online content delivery process requires an Internet connection. However, access to the broadband has never been a readily available resource in many regions. In Africa, poor and no networks are yet predominantly experienced by Internet users, frequently causing offline each moment a digital device disconnect from a network. As a result, a learning process is always disrupted, delayed and terminated in such regions. This paper raises the concern of E-Learning in poor and low bandwidths, in fact, it highlights the needs for an Offline-Enabled mode. The paper also explores technical approaches beamed to enhance the user experience inWeb-based E-Learning, particular in Africa.
Recently, Kocyan & Wiland-Szymańska (2016) have published a thorough research article on one of the outstanding members of the family Hypoxidaceae on the Seychelles, which resulted in the raise of a new genus (Friedmannia Kocyan & Wiland-Szymańska 2016: 60) to accommodate the former Curculigo seychellensis Bojer ex Baker (1877: 368). However, it has turned out that the name Friedmannia Chantanachat & Bold (1962: 45) already exists in literature for a green alga, which renders the new hypoxid genus illegitimate (Melbourne Code; McNeill et al. 2012). Therefore, we assign a new generic epithet to Curculigo seychellensis.
Editorial
(2017)
Background: Infliximab (IFX), an anti-TNF monoclonal antibody approved for the treatment of inflammatory bowel disease, is dosed per kg body weight (BW). However, the rationale for body size adjustment has not been unequivocally demonstrated [1], and first attempts to improve IFX therapy have been undertaken [2]. The aim of our study was to assess the impact of different dosing strategies (i.e. body size-adjusted and fixed dosing) on drug exposure and pharmacokinetic (PK) target attainment. For this purpose, a comprehensive simulation study was performed, using patient characteristics (n=116) from an in-house clinical database.
Methods: IFX concentration-time profiles of 1000 virtual, clinically representative patients were generated using a previously published PK model for IFX in patients with Crohn's disease [3]. For each patient 1000 profiles accounting for PK variability were considered. The IFX exposure during maintenance treatment after the following dosing strategies was compared: i) fixed dose, and per ii) BW, iii) lean BW (LBW), iv) body surface area (BSA), v) height (HT), vi) body mass index (BMI) and vii) fat-free mass (FFM)). For each dosing strategy the variability in maximum concentration Cmax, minimum concentration Cmin (= C8weeks) and area under the concentration-time curve (AUC), as well as percent of patients achieving the PK target, Cmin=3 μg/mL [4] were assessed.
Results: For all dosing strategies the variability of Cmin (CV ≈110%) was highest, compared to Cmax and AUC, and was of similar extent regardless of dosing strategy. The proportion of patients reaching the PK target (≈⅓ was approximately equal for all dosing strategies.
Gaussianity Fair
(2017)