Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures

The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu

A Reference Structure for Modular Model-based Analyses

Kontext: In dieser Arbeit haben wir die Evolvierbarkeit, Verständlichkeit und Wiederverwendbarkeit von modellbasierten Analysen untersucht. Darum untersuchten wir die Wechselbeziehungen zwischen Modellen und Analysen, insbesondere die Struktur und Abhängigkeiten von Artefakten und die Dekomposition und Komposition von modellbasierten Analysen. Herausforderungen: Softwareentwickler verwenden Modelle von Softwaresystemen, um die Evolvierbarkeit und Wiederverwendbarkeit eines Architekturentwurfs zu bestimmen. Diese Modelle ermöglichen die Softwarearchitektur zu analysieren, bevor die erste Zeile Code geschreiben wird. Aufgrund evolutionärer Veränderungen sind modellbasierte Analysen jedoch auch anfällig für eine Verschlechterung der Evolvierbarkeit, Verständlichkeit und Wiederverwendbarkeit. Diese Probleme lassen sich auf die Ko-Evolution von Modellierungssprache und Analyse zurückführen. Der Zweck einer Analyse ist die systematische Untersuchung bestimmter Eigenschaften eines zu untersuchenden Systems. Nehmen wir zum Beispiel an, dass Softwareentwickler neue Eigenschaften eines Softwaresystems analysieren wollen. In diesem Fall müssen sie Merkmale der Modellierungssprache und die entsprechenden modellbasierten Analysen anpassen, bevor sie neue Eigenschaften analysieren können. Merkmale in einer modellbasierten Analyse sind z.\,B. eine Analysetechnik, die eine solche Qualitätseigenschaft analysiert. Solche Änderungen führen zu einer erhöhten Komplexität der modellbasierten Analysen und damit zu schwer zu pflegenden modellbasierten Analysen. Diese steigende Komplexität verringert die Verständlichkeit der modellbasierten Analysen. Infolgedessen verlängern sich die Entwicklungszyklen, und die Softwareentwickler benötigen mehr Zeit, um das Softwaresystem an veränderte Anforderungen anzupassen. Stand der Technik: Derzeitige Ansätze ermöglichen die Kopplung von Analysen auf einem System oder über verteilte Systeme hinweg. Diese Ansätze bieten die technische Struktur für die Kopplung von Simulationen, nicht aber eine Struktur wie Komponenten (de)komponiert werden können. Eine weitere Herausforderung beim Komponieren von Analysen ist der Verhaltensaspekt, der sich darin äußert, wie sich die Analysekomponenten gegenseitig beeinflussen. Durch die Synchronisierung jeder beteiligten Simulation erhöht die Modularisierung von Simulationen den Kommunikationsbedarf. Derzeitige Ansätze erlauben es, den Kommunikationsaufwand zu reduzieren; allerdings werden bei diesen Ansätzen die Dekomposition und Komposition dem Benutzer überlassen. Beiträge: Ziel dieser Arbeit ist es, die Evolvierbarkeit, Verständlichkeit und Wiederverwendbarkeit von modellbasierten Analysen zu verbessern. Zu diesem Zweck wird die Referenzarchitektur für domänenspezifische Modellierungssprachen als Grundlage genommen und die Übertragbarkeit der Struktur der Referenzarchitektur auf modellbasierte Analysen untersucht. Die geschichtete Referenzarchitektur bildet die Abhängigkeiten der Analysefunktionen und Analysekomponenten ab, indem sie diese bestimmten Schichten zuordnet. Wir haben drei Prozesse für die Anwendung der Referenzarchitektur entwickelt: (i) Refactoring einer bestehenden modellbasierten Analyse, (ii) Entwurf einer neuen modellbasierten Analyse und (iii) Erweiterung einer bestehenden modellbasierten Analyse. Zusätzlich zur Referenzarchitektur für modellbasierte Analysen haben wir wiederkehrende Strukturen identifiziert, die zu Problemen bei der Evolvierbarkeit, Verständlichkeit und Wiederverwendbarkeit führen; in der Literatur werden diese wiederkehrenden Strukturen auch als Bad Smells bezeichnet. Wir haben etablierte modellbasierte Analysen untersucht und dreizehn Bad Smells identifiziert und spezifiziert. Neben der Spezifizierung der Bad Smells bieten wir einen Prozess zur automatischen Identifizierung dieser Bad Smells und Strategien für deren Refactoring, damit Entwickler diese Bad Smells vermeiden oder beheben können. In dieser Arbeit haben wir auch eine Modellierungssprache zur Spezifikation der Struktur und des Verhaltens von Simulationskomponenten entwickelt. Simulationen sind Analysen, um ein System zu untersuchen, wenn das Experimentieren mit dem bestehenden System zu zeitaufwändig, zu teuer, zu gefährlich oder einfach unmöglich ist, weil das System (noch) nicht existiert. Entwickler können die Spezifikation nutzen, um Simulationskomponenten zu vergleichen und so identische Komponenten zu identifizieren. Validierung: Die Referenzarchitektur für modellbasierte Analysen, haben wir evaluiert, indem wir vier modellbasierte Analysen in die Referenzarchitektur überführt haben. Wir haben eine szenariobasierte Evaluierung gewählt, die historische Änderungsszenarien aus den Repositories der modellbasierten Analysen ableitet. In der Auswertung können wir zeigen, dass sich die Evolvierbarkeit und Verständlichkeit durch die Bestimmung der Komplexität, der Kopplung und der Kohäsion verbessert. Die von uns verwendeten Metriken stammen aus der Informationstheorie, wurden aber bereits zur Bewertung der Referenzarchitektur für DSMLs verwendet. Die Bad Smells, die durch die Co-Abhängigkeit von modellbasierten Analysen und ihren entsprechenden DSMLs entstehen, haben wir evaluiert, indem wir vier modellbasierte Analysen nach dem Auftreten unserer schlechten Gerüche durchsucht und dann die gefundenen Bad Smells behoben haben. Wir haben auch eine szenariobasierte Auswertung gewählt, die historische Änderungsszenarien aus den Repositories der modellbasierten Analysen ableitet. Wir können zeigen, dass die Bad Smells die Evolvierbarkeit und Verständlichkeit negativ beeinflussen, indem wir die Komplexität, Kopplung und Kohäsion vor und nach der Refaktorisierung bestimmen. Den Ansatz zum Spezifizieren und Finden von Komponenten modellbasierter Analysen haben wir evaluiert, indem wir Komponenten von zwei modellbasierten Analysen spezifizieren und unseren Suchalgorithmus verwenden, um ähnliche Analysekomponenten zu finden. Die Ergebnisse der Evaluierung zeigen, dass wir in der Lage sind, ähnliche Analysekomponenten zu finden und dass unser Ansatz die Suche nach Analysekomponenten mit ähnlicher Struktur und ähnlichem Verhalten und damit die Wiederverwendung solcher Komponenten ermöglicht. Nutzen: Die Beiträge unserer Arbeit unterstützen Architekten und Entwickler bei ihrer täglichen Arbeit, um wartbare und wiederverwendbare modellbasierte Analysen zu entwickeln. Zu diesem Zweck stellen wir eine Referenzarchitektur bereit, die die modellbasierte Analyse und die domänenspezifische Modellierungssprache aufeinander abstimmt und so die Koevolution erleichtert. Zusätzlich zur Referenzarchitektur bieten wir auch Refaktorisierungsoperationen an, die es Architekten und Entwicklern ermöglichen, eine bestehende modellbasierte Analyse an die Referenzarchitektur anzupassen. Zusätzlich zu diesem technischen Aspekt haben wir drei Prozesse identifiziert, die es Architekten und Entwicklern ermöglichen, eine neue modellbasierte Analyse zu entwickeln, eine bestehende modellbasierte Analyse zu modularisieren und eine bestehende modellbasierte Analyse zu erweitern. Dies geschieht natürlich so, dass die Ergebnisse mit der Referenzarchitektur konform sind. Darüber hinaus ermöglicht unsere Spezifikation den Entwicklern, bestehende Simulationskomponenten zu vergleichen und sie bei Bedarf wiederzuverwenden. Dies erspart den Entwicklern die Neuimplementierung von Komponenten

Architecture for intelligent power systems management, optimization, and storage.

The management of power and the optimization of systems generating and using power are critical technologies. A new architecture is developed to advance the current state of the art by providing an intelligent and autonomous solution for power systems management. The architecture is two-layered and implements a decentralized approach by defining software objects, similar to software agents, which provide for local optimization of power devices such as power generating, storage, and load devices. These software device objects also provide an interface to a higher level of optimization. This higher level of optimization implements the second layer in a centralized approach by coordinating the individual software device objects with an intelligent expert system thus resulting in architecture for total system power management. In this way, the architecture acquires the benefits of both the decentralized and centralized approaches. The architecture is designed to be portable, scalable, simple, and autonomous, with respect to devices and missions. Metrics for evaluating these characteristics are also defined. Decentralization achieves scalability and simplicity through modularization using software device objects that can be added and deleted as modules based on the devices of the power system are being optimized. Centralization coordinates these software device objects to bring autonomy and intelligence of the whole power system and mission to the architecture. The centralization approach is generic since it always coordinates software device objects; therefore it becomes another modular component of the architecture. Three example implementations illustrate the evolution of this power management system architecture. The first implementation is a coal-fired power generating station that utilized a neural network optimization for the reduction of nitrogen oxide emissions. This illustrates the limitations of this type of black-box optimization and serves as a motivation for developing a more functional architecture. The second implementation is of a hydro-generating power station where a white-box, software agent approach illustrates some of the benefits and provides initial justification of moving towards the proposed architecture. The third implementation applies the architecture to a vehicle to grid application where the previous hydro-generating application is ported and a new hybrid vehicle application is defined. This demonstrates portability and scalability in the architecture, and linking these two applications demonstrates autonomy. The simplicity of building this application is also evaluated

Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ($T_{\mathrm{c}}$) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their $T_{\mathrm{c}}$ values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.Comment: 17 pages, 7 figure

Support for collaborative component-based software engineering

Collaborative system composition during design has been poorly supported by traditional CASE tools (which have usually concentrated on supporting individual projects) and almost exclusively focused on static composition. Little support for maintaining large distributed collections of heterogeneous software components across a number of projects has been developed. The CoDEEDS project addresses the collaborative determination, elaboration, and evolution of design spaces that describe both static and dynamic compositions of software components from sources such as component libraries, software service directories, and reuse repositories. The GENESIS project has focussed, in the development of OSCAR, on the creation and maintenance of large software artefact repositories. The most recent extensions are explicitly addressing the provision of cross-project global views of large software collections and historical views of individual artefacts within a collection. The long-term benefits of such support can only be realised if OSCAR and CoDEEDS are widely adopted and steps to facilitate this are described. This book continues to provide a forum, which a recent book, Software Evolution with UML and XML, started, where expert insights are presented on the subject. In that book, initial efforts were made to link together three current phenomena: software evolution, UML, and XML. In this book, focus will be on the practical side of linking them, that is, how UML and XML and their related methods/tools can assist software evolution in practice. Considering that nowadays software starts evolving before it is delivered, an apparent feature for software evolution is that it happens over all stages and over all aspects. Therefore, all possible techniques should be explored. This book explores techniques based on UML/XML and a combination of them with other techniques (i.e., over all techniques from theory to tools). Software evolution happens at all stages. Chapters in this book describe that software evolution issues present at stages of software architecturing, modeling/specifying, assessing, coding, validating, design recovering, program understanding, and reusing. Software evolution happens in all aspects. Chapters in this book illustrate that software evolution issues are involved in Web application, embedded system, software repository, component-based development, object model, development environment, software metrics, UML use case diagram, system model, Legacy system, safety critical system, user interface, software reuse, evolution management, and variability modeling. Software evolution needs to be facilitated with all possible techniques. Chapters in this book demonstrate techniques, such as formal methods, program transformation, empirical study, tool development, standardisation, visualisation, to control system changes to meet organisational and business objectives in a cost-effective way. On the journey of the grand challenge posed by software evolution, the journey that we have to make, the contributory authors of this book have already made further advances

Comparison of imaging with sub-wavelength resolution in the canalization and resonant tunnelling regimes

We compare the properties of subwavelength imaging in the visible wavelength range for metal-dielectric multilayers operating in the canalization and the resonant tunnelling regimes. The analysis is based on the transfer matrix method and time domain simulations. We show that Point Spread Functions for the first two resonances in the canalization regime are approximately Gaussian in shape. Material losses suppress transmission for higher resonances, regularise the PSF but do not compromise the resolution. In the resonant tunnelling regime, the MTF may dramatically vary in their phase dependence. Resulting PSF may have a sub-wavelength thickness as well as may be broad with multiple maxima and a rapid phase modulation. We show that the width of PSF may be reduced by further propagation in free space, and we provide arguments to explain this surprising observation.Comment: 17 pages,12 figure

Geographic Information Systems for Real-Time Environmental Sensing at Multiple Scales

The purpose of this investigation was to design, implement, and apply a real-time geographic information system for data intensive water resource research and management. The research presented is part of an ongoing, interdisciplinary research program supporting the development of the Intelligent River® observation instrument. The objectives of this research were to 1) design and describe software architecture for a streaming environmental sensing information system, 2) implement and evaluate the proposed information system, and 3) apply the information system for monitoring, analysis, and visualization of an urban stormwater improvement project located in the City of Aiken, South Carolina, USA. This research contributes to the fields of software architecture and urban ecohydrology. The first contribution is a formal architectural description of a streaming environmental sensing information system. This research demonstrates the operation of the information system and provides a reference point for future software implementations. Contributions to urban ecohydrology are in three areas. First, a characterization of soil properties for the study region of the City of Aiken, SC is provided. The analysis includes an evaluation of spatial structure for soil hydrologic properties. Findings indicate no detectable structure at the scales explored during the study. The second contribution to ecohydrology comes from a long-term, continuous monitoring program for bioinfiltration basin structures located in the study area. Results include an analysis of soil moisture dynamics based on data collected at multiple depths with high spatial and temporal resolution. A novel metric is introduced to evaluate the long-term performance of bioinfiltration basin structures based on soil moisture observation data. Findings indicate a decrease in basin performance over time for the monitored sites. The third contribution to the field of ecohydrology is the development and application of a spatially and temporally explicit rainfall infiltration and excess model. The model enables the simulation and visualization of bioinfiltration basin hydrologic response at within-catchment scales. The model is validated against observed soil moisture data. Results include visualizations and stormwater volume calculations based on measured versus predicted bioinfiltration basin performance over time

Using real options to select stable Middleware-induced software architectures

The requirements that force decisions towards building distributed system architectures are usually of a non-functional nature. Scalability, openness, heterogeneity, and fault-tolerance are examples of such non-functional requirements. The current trend is to build distributed systems with middleware, which provide the application developer with primitives for managing the complexity of distribution, system resources, and for realising many of the non-functional requirements. As non-functional requirements evolve, the `coupling' between the middleware and architecture becomes the focal point for understanding the stability of the distributed software system architecture in the face of change. It is hypothesised that the choice of a stable distributed software architecture depends on the choice of the underlying middleware and its flexibility in responding to future changes in non-functional requirements. Drawing on a case study that adequately represents a medium-size component-based distributed architecture, it is reported how a likely future change in scalability could impact the architectural structure of two versions, each induced with a distinct middleware: one with CORBA and the other with J2EE. An option-based model is derived to value the flexibility of the induced-architectures and to guide the selection. The hypothesis is verified to be true for the given change. The paper concludes with some observations that could stimulate future research in the area of relating requirements to software architectures