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Urban platforms are essential for smart and sustainable city planning and operation. Today they are mostly designed to handle and connect large urban data sets from very different domains. Modelling and optimisation functionalities are usually not part of the cities software infrastructure. However, they are considered crucial for transformation scenario development and optimised smart city operation. The work discusses software architecture concepts for such urban platforms and presents case study results on the building sector modelling, including urban data analysis and visualisation. Results from a case study in New York are presented to demonstrate the implementation status.
An important shift in software delivery is the definition of a cloud service as an independently deployable unit by following the microservices architectural style. Container virtualization facilitates development and deployment by ensuring independence from the runtime environment. Thus, cloud services are built as container based systems - a set of containers that control the lifecycle of software and middleware components. However, using containers leads to a new paradigm for service development and operation: Self service environments enable software developers to deploy and operate container based systems on their own - you build it, you run it. Following this approach, more and more operational aspects are transferred towards the responsibility of software developers. In this work, we propose a concept for self-adaptive cloud services based on container virtualization in line with the microservices architectural style and present a model-based approach that assists software developers in building these services. Based on operational models specified by developers, the mechanisms required for self-adaptation are automatically generated. As a result, each container automatically adapts itself in a reactive, decentralized manner. We evaluate a prototype which leverages the emerging TOSCA standard to specify operational behavior in a portable manner.
In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers’ lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.
In recent years, the parallel computing community has shown increasing interest in leveraging cloud resources for executing parallel applications. Clouds exhibit several fundamental features of economic value, like on-demand resource provisioning and a pay-per-use model. Additionally, several cloud providers offer their resources with significant discounts; however, possessing limited availability. Such volatile resources are an auspicious opportunity to reduce the costs arising from computations, thus achieving higher cost efficiency. In this paper, we propose a cost model for quantifying the monetary costs of executing parallel applications in cloud environments, leveraging volatile resources. Using this cost model, one is able to determine a configuration of a cloud-based parallel system that minimizes the total costs of executing an application.
Information and communication technologies support telemedicine to lower health access barriers and to provide better health care. While the potential in Active Assisted Living (AAL) is increasing, it is difficult to evaluate its benefits for the user, and it requires coordinated actions to launch it. The European Commission’s action plan 2012–2020 provides a roadmap to patient empowerment and healthcare, to link up devices and technologies, and to invest in research towards the personalized medicine of the future. As a quickly developing area in medicine, telemonitoring is a demanding field in research and development. Telemonitoring is an essential component of personalized medicine, where health providers can obtain precise information on outcare or chronic patients to improve diagnosis and therapy and also help healthy persons with prevention support. Telemonitoring combines mobile and wearable devices with the personal AAL home environment, a private or (partly) supervised home, most often called ’smart home’. The focus of this workshop is on new hardware and software solutions specifically designed to be applicable in AAL environments to empower patients. This workshop presents system-oriented solutions covering wearable and AAL-embedded devices, computer science infrastructure both at the users’ and the medical premises, to handle the data and decision support systems to support diagnose and treatment.
Integrating tools and applications into a clinically useful system for individual continuous health data surveillance requires an architecture considering all relevant medical and technical conditions. Therefore, the requirements of an integrated system including a health app to collect and monitor sensor data to support personalized medicine are analyzed. The structure and behavior of the system are defined regarding the specific health use cases and scenarios. A vendor-independent architecture, which enables the collection of vital data from arbitrary wearables using a smartphone, is presented. The data is centrally managed and processed by attending physicians. The modular architecture allows the system to extend to new scenarios, data formats, etc. A prototypical implementation of the system shows the feasibility of the approach.
In this paper, we deal with optimizing the monetary costs of executing parallel applications in cloud-based environments. Specifically, we investigate on how scalability characteristics of parallel applications impact the total costs of computations. We focus on a specific class of irregularly structured problems, where the scalability typically depends on the input data. Consequently, dynamic optimization methods are required for minimizing the costs of computation. For quantifying the total monetary costs of individual parallel computations, the paper presents a cost model that considers the costs for the parallel infrastructure employed as well as the costs caused by delayed results. We discuss a method for dynamically finding the number of processors for which the total costs based on our cost model are minimal. Our extensive experimental evaluation gives detailed insights into the performance characteristics of our approach.
Parallel applications are the computational backbone of major industry trends and grand challenges in science. Whereas these applications are typically constructed for dedicated High Performance Computing clusters and supercomputers, the cloud emerges as attractive execution environment, which provides on-demand resource provisioning and a pay-per-use model. However, cloud environments require specific application properties that may restrict parallel application design. As a result, design trade-offs are required to simultaneously maximize parallel performance and benefit from cloud-specific characteristics.
In this paper, we present a novel approach to assess the cloud readiness of parallel applications based on the design decisions made. By discovering and understanding the implications of these parallel design decisions on an application’s cloud readiness, our approach supports the migration of parallel applications to the cloud.We introduce an assessment procedure, its underlying meta model, and a corresponding instantiation to structure this multi-dimensional design space. For evaluation purposes, we present an extensive case study comprising three parallel applications and discuss their cloud readiness based on our approach.
Data analytics tasks on large datasets are computationally intensive and often demand the compute power of cluster environments. Yet, data cleansing, preparation, dataset characterization and statistics or metrics computation steps are frequent. These are mostly performed ad hoc, in an explorative manner and mandate low response times. But, such steps are I/O intensive and typically very slow due to low data locality, inadequate interfaces and abstractions along the stack. These typically result in prohibitively expensive scans of the full dataset and transformations on interface boundaries.
In this paper, we examine R as analytical tool, managing large persistent datasets in Ceph, a wide-spread cluster file-system. We propose nativeNDP – a framework for Near Data Processing that pushes down primitive R tasks and executes them in-situ, directly within the storage device of a cluster-node. Across a range of data sizes, we show that nativeNDP is more than an order of magnitude faster than other pushdown alternatives.
Due to frequently changing requirements, the internal structure of cloud services is highly dynamic. To ensure flexibility, adaptability, and maintainability for dynamically evolving services, modular software development has become the dominating paradigm. By following this approach, services can be rapidly constructed by composing existing, newly developed and publicly available third-party modules. However, newly added modules might be unstable, resource-intensive, or untrustworthy. Thus, satisfying non-functional requirements such as reliability, efficiency, and security while ensuring rapid release cycles is a challenging task. In this paper, we discuss how to tackle these issues by employing container virtualization to isolate modules from each other according to a specification of isolation constraints. We satisfy non-functional requirements for cloud services by automatically transforming the modules comprised into a container-based system. To deal with the increased overhead that is caused by isolating modules from each other, we calculate the minimum set of containers required to satisfy the isolation constraints specified. Moreover, we present and report on a prototypical transformation pipeline that automatically transforms cloud services developed based on the Java Platform Module System into container-based systems.
With on-demand access to compute resources, pay-per-use, and elasticity, the cloud evolved into an attractive execution environment for High Performance Computing (HPC). Whereas elasticity, which is often referred to as the most beneficial cloud-specific property, has been heavily used in the context of interactive (multi-tier) applications, elasticity-related research in the HPC domain is still in its infancy. Existing parallel computing theory as well as traditional metrics to analytically evaluate parallel systems do not comprehensively consider elasticity, i.e., the ability to control the number of processing units at runtime. To address these issues, we introduce a conceptual framework to understand elasticity in the context of parallel systems, define the term elastic parallel system, and discuss novel metrics for both elasticity control at runtime as well as the ex post performance evaluation of elastic parallel systems. Based on the conceptual framework, we provide an in depth analysis of existing research in the field to describe the state-of-the art and compile our findings into a research agenda for future research on elastic parallel systems.
With the capability of employing virtually unlimited compute resources, the cloud evolved into an attractive execution environment for applications from the High Performance Computing (HPC) domain. By means of elastic scaling, compute resources can be provisioned and decommissioned at runtime. This gives rise to a new concept in HPC: Elasticity of parallel computations. However, it is still an open research question to which extent HPC applications can benefit from elastic scaling and how to leverage elasticity of parallel computations. In this paper, we discuss how to address these challenges for HPC applications with dynamic task parallelism and present TASKWORK, a cloud-aware runtime system based on our findings. TASKWORK enables the implementation of elastic HPC applications by means of higher level development frameworks and solves corresponding coordination problems based on Apache ZooKeeper. For evaluation purposes, we discuss a development framework for parallel branch-and-bound based on TASKWORK, show how to implement an elastic HPC application, and report on measurements with respect to parallel efficiency and elastic scaling.
Database Management Systems (DBMS) need to handle large updatable datasets in on-line transaction processing (OLTP) workloads. Most modern DBMS provide snapshots of data in multi-version concurrency control (MVCC) transaction management scheme. Each transaction operates on a snapshot of the database, which is calculated from a set of tuple versions. High parallelism and resource-efficient append-only data placement on secondary storage is enabled. One major issue in indexing tuple versions on modern hardware technologies is the high write amplification for tree-indexes.
Partitioned B-Trees (PBT) [5] is based on the structure of the ubiquitous B+ Tree [8]. They achieve a near optimal write amplification and beneficial sequential writes on secondary storage. Yet they have not been implemented in a MVCC enabled DBMS to date.
In this paper we present the implementation of PBTs in PostgreSQL extended with SIAS. Compared to PostgreSQL’s B+–Trees PBTs have 50% better transaction throughput under TPC-C and a 30% improvement to standard PostgreSQL with Heap-Only Tuples.
In the present tutorial we perform a cross-cut analysis of database storage management from the perspective of modern storage technologies. We argue that neither the design of modern DBMS, nor the architecture of modern storage technologies are aligned with each other. Moreover, the majority of the systems rely on a complex multi-layer and compatibility oriented storage stack. The result is needlessly suboptimal DBMS performance, inefficient utilization, or significant write amplification due to outdated abstractions and interfaces. In the present tutorial we focus on the concept of native storage, which is storage operated without intermediate abstraction layers over an open native storage interface and is directly controlled by the DBMS.
Enterprises are presently transforming their strategy, culture, processes, and their information systems to become more digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things or mobile systems. Since years a lot of new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. This has a strong impact for architecting digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of system architectures defines the moving context for adaptable systems, which are essential to enable the digital transformation. In this paper, we are focusing on a decision-oriented architectural composition approach to support the transformation for digital services and products.
Revenue management information systems are very important in the hospitality sector. Revenue decisions can be better prepared based on different information from different information systems and decision strategies. There is a lack of research about the usage of such systems in small and medium-sized hotels and architectural configurations. Our paper empirically shows the current development of revenue information systems. Furthermore, we define future developments and requirements to improve such systems and the architectural base.
Ganz gleich, ob im privaten oder beruflichen Alltag, begleiten uns digitale Medien heute nahezu überall. Dabei dienen sie nicht nur zur Unterhaltung, sondern helfen uns, Arbeitsabläufe effizienter und produktiver durchzuführen. Doch die Arbeit des Menschen ist bei Weitem nicht überflüssig geworden. Durch die steigenden Anforderungen ist die Nachfrage nach qualifiziertem Fachpersonal heute höher denn je. Währenddessen müssen Mitarbeiter in der Lage sein, mit der rasanten Entwicklung neuer Produkte und Technologien Schritt zu halten. Dabei ist eine qualitative Aus- und Weiterbildung unumgänglich. Beginnend mit der Bildung von Medienkompetenz in Schulen bis hin zur Fach- und Berufsbildung sowie beruflichen Weiterbildung, muss der Umgang mit digitalen Technologien gelehrt sein. Darüber hinaus bieten diese Technologien neue Potenziale zur Verbesserung von Bildungskonzepten und können zudem dabei helfen, den Lernerfolg zu steigern.
Diese Arbeit beschäftigt sich mit der Evaluation einer VR-basierten Lernumgebung und untersucht mögliche Auswirkungen auf den Lernerfolg durch die verkörperte Darstellung eines virtuellen Instruktors. Dazu wurde die technische Implementierung einer kollaborativen Lernumgebung vorgenommen, mit welcher anschließend eine Versuchsreihe mit 16 Probanden durchgeführt wurde. Im Hinblick auf eine mögliche Steigerung der Effizienz in der eigenständigen Bewältigung von Montageaufgaben nach unterschiedlichen Instruktionsarten, wurden keine signifikanten Leistungsverbesserungen festgestellt.
Die Erholung unseres Körpers und Gehirns von Müdigkeit ist direkt abhängig von der Qualität des Schlafes, die aus den Ergebnissen einer Schlafstudie ermittelt werden kann. Die Klassifizierung der Schlafstadien ist der erste Schritt dieser Studie und beinhaltet die Messung von Biovitaldaten und deren weitere Verarbeitung. Das non-invasive Schlafanalyse-System basiert auf einem Hardware-Sensornetz aus 24 Drucksensoren, das die Schlafphasenerkennung ermöglicht. Die Drucksensoren sind mit einem energieeffizienten Mikrocontroller über einen systemweiten Bus mit Adressarbitrierung verbunden. Ein wesentlicher Unterschied dieses Systems im Vergleich zu anderen Ansätzen ist die innovative Art, die Sensoren unter der Matratze zu platzieren. Diese Eigenschaft erleichtert die kontinuierliche Nutzung des Systems ohne fühlbaren Einfluss auf das gewohnte Bett. Das System wurde getestet, indem Experimente durchgeführt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die ersten Ergebnisse weisen auf das Potenzial hin, nicht nur Atemfrequenz und Körperbewegung, sondern auch Herzfrequenz zu erfassen.
Software evolvability is an important quality attribute, yet one difficult to grasp. A certain base level of it is allegedly provided by service- and microservice-based systems, but many software professionals lack systematic understanding of the reasons and preconditions for this. We address this issue via the proxy of architectural modifiability tactics. By qualitatively mapping principles and patterns of Service Oriented Architecture (SOA) and microservices onto tactics and analyzing the results, we cannot only generate insights into service-oriented evolution qualities, but can also provide a modifiability comparison of the two popular service-based architectural styles. The results suggest that both SOA and microservices possess several inherent qualities beneficial for software evolution. While both focus strongly on loose coupling and encapsulation, there are also differences in the way they strive for modifiability (e.g. governance vs. evolutionary design). To leverage the insights of this research, however, it is necessary to find practical ways to incorporate the results as guidance into the software development process.