Proxcache: A new cache deployment strategy in information-centric network for mitigating path and content redundancy

One of the promising paradigms for resource sharing with maintaining the basic Internet semantics is the Information-Centric Networking (ICN). ICN distinction with the current Internet is its ability to refer contents by names with partly dissociating the host-to-host practice of Internet Protocol addresses. Moreover, content caching in ICN is the major action of achieving content networking to reduce the amount of server access. The current caching practice in ICN using the Leave Copy Everywhere (LCE) progenerate problems of over deposition of contents known as content redundancy, path redundancy, lesser cache-hit rates in heterogeneous networks and lower content diversity. This study proposes a new cache deployment strategy referred to as ProXcache to acquire node relationships using hyperedge concept of hypergraph for cache positioning. The study formulates the relationships through the path and distance approximation to mitigate content and path redundancy. The study adopted the Design Research Methodology approach to achieve the slated research objectives. ProXcache was investigated using simulation on the Abilene, GEANT and the DTelekom network topologies for LCE and ProbCache caching strategies with the Zipf distribution to differ content categorization. The results show the overall content and path redundancy are minimized with lesser caching operation of six depositions per request as compared to nine and nineteen for ProbCache and LCE respectively. ProXcache yields better content diversity ratio of 80% against 20% and 49% for LCE and ProbCache respectively as the cache sizes varied. ProXcache also improves the cache-hit ratio through proxy positions. These thus, have significant influence in the development of the ICN for better management of contents towards subscribing to the Future Internet

Sensae Console - Platforma de support para serviços baseados em IoT

Today there are more smart devices than people. The number of devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion devices in 2030. The Internet of Things (IoT) is the connection of millions of smart devices and sensors connected to the Internet. These connected devices and sensors collect and share data for use and analysis by many organizations. Some examples of intelligent connected sensors are: GPS asset tracking, parking spots, refrigerator thermostats, soil condition and many others. The limit of different objects that can become intelligent sensors is limited only by our imagination. But these devices are mostly useless without a platform to analyze, store and present the aggregated data into business-oriented information. Recently, several platforms have emerged to address this need and help companies/governments to increase efficiency, cut on operational costs and improve safety. Sadly, most of these platforms are tailor made for the devices that the company offers. This dissertation presents the (Sensae Console) platform that enables and promotes the development of IoT-based business-oriented applications. This platform attempts to be device-neutral, IoT middleware-neutral and provide flexible upstream integration and hosting options while providing a simple and concise data streaming Application Programming Interface (API). Three IoT-based business-oriented applications built on top of the Sensae Console platform are presented as Proof of Concept (PoC) of its capabilities.Atualmente, existem mais sensores inteligentes do que pessoas. O número de sensores em todo o mundo deve quase triplicar de 8,74 bilhões em 2020 para mais de 25,4 bilhões em 2030. O conceito de IoT está relacionado com a interação entre milhões de dispositivos inteligentes através da Internet. Estes dispositivos e sensores conectados recolhem e disponibilizam dados para uso e análise por parte de muitas organizações. Alguns exemplos de sensores inteligentes e seus usos são: dispositivos GPS para rastreamento de ativos, monitorização de vagas de estacionamento, termostatos em arcas frigoríficas, condição do solo e muitos outros. O número de diferentes objetos que podem vir-se a tornar sensores inteligentes é limitado apenas pela nossa imaginação. Mas estes dispositivos são praticamente inúteis sem uma plataforma para analisar, armazenar e apresentar os dados agregados em informação relevante para o negócio em questão. Recentemente, várias plataformas surgiram para responder a essa necessidade e ajudar empresas/governos a aumentar a sua eficiência, reduzir custos operacionais e melhorar a segurança dos espaços e negócios. Infelizmente, a maioria dessas plataformas é feita à medida para os dispositivos que a empresa em questão oferece. Esta tese apresenta uma plataforma (Sensae Console) focada em que propiciar a criação de aplicações relacionados com IoT para negócios específicos. Esta plataforma procura ser agnóstica em relação aos dispositivos inteligentes e middleware de IoT usados por terceiros, oferece variadas e flexíveis opções de integração e hosting como também uma API de streaming simples e concisa. Três aplicações relacionadas com IoT, orientadas ao seu negócio e construídas com base na plataforma Sensae Console são apresentadas como provas de conceito das capacidades da plataforma

Effizienz in Cluster-Datenbanksystemen - Dynamische und Arbeitslastberücksichtigende Skalierung und Allokation

Database systems have been vital in all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. Nevertheless, database management systems tend to be static in terms of size and performance which makes scaling a difficult and expensive task. Because of performance and especially cost advantages more and more installed systems have a shared nothing cluster architecture. Due to the massive parallelism of the hardware programming paradigms from high performance computing are translated into data processing. Database research struggles to keep up with this trend. A key feature of traditional database systems is to provide transparent access to the stored data. This introduces data dependencies and increases system complexity and inter process communication. Therefore, many developers are exchanging this feature for a better scalability. However, explicitly managing the data distribution and data flow requires a deep understanding of the distributed system and reduces the possibilities for automatic and autonomic optimization. In this thesis we present an approach for database system scaling and allocation that features good scalability although it keeps the data distribution transparent. The first part of this thesis analyzes the challenges and opportunities for self-scaling database management systems in cluster environments. Scalability is a major concern of Internet based applications. Access peaks that overload the application are a financial risk. Therefore, systems are usually configured to be able to process peaks at any given moment. As a result, server systems often have a very low utilization. In distributed systems the efficiency can be increased by adapting the number of nodes to the current workload. We propose a processing model and an architecture that allows efficient self-scaling of cluster database systems. In the second part we consider different allocation approaches. To increase the efficiency we present a workload-aware, query-centric model. The approach is formalized; optimal and heuristic algorithms are presented. The algorithms optimize the data distribution for local query execution and balance the workload according to the query history. We present different query classification schemes for different forms of partitioning. The approach is evaluated for OLTP and OLAP style workloads. It is shown that variants of the approach scale well for both fields of application. The third part of the thesis considers benchmarks for large, adaptive systems. First, we present a data generator for cloud-sized applications. Due to its architecture the data generator can easily be extended and configured. A key feature is the high degree of parallelism that makes linear speedup for arbitrary numbers of nodes possible. To simulate systems with user interaction, we have analyzed a productive online e-learning management system. Based on our findings, we present a model for workload generation that considers the temporal dependency of user interaction.Datenbanksysteme sind seit langem die Grundlage für alle Arten von Informationsverarbeitung. In den letzten Jahren ist das Datenaufkommen selbst in kleinen Projekten dramatisch angestiegen. Dennoch sind viele Datenbanksysteme statisch in Bezug auf ihre Kapazität und Verarbeitungsgeschwindigkeit was die Skalierung aufwendig und teuer macht. Aufgrund der guten Geschwindigkeit und vor allem aus Kostengründen haben immer mehr Systeme eine Shared-Nothing-Architektur, bestehen also aus unabhängigen, lose gekoppelten Rechnerknoten. Da dieses Konstruktionsprinzip einen sehr hohen Grad an Parallelität aufweist, werden zunehmend Programmierparadigmen aus dem klassischen Hochleistungsrechen für die Informationsverarbeitung eingesetzt. Dieser Trend stellt die Datenbankforschung vor große Herausforderungen. Eine der grundlegenden Eigenschaften traditioneller Datenbanksysteme ist der transparente Zugriff zu den gespeicherten Daten, der es dem Nutzer erlaubt unabhängig von der internen Organisation auf die Daten zuzugreifen. Die resultierende Unabhängigkeit führt zu Abhängigkeiten in den Daten und erhöht die Komplexität der Systeme und der Kommunikation zwischen einzelnen Prozessen. Daher wird Transparenz von vielen Entwicklern für eine bessere Skalierbarkeit geopfert. Diese Entscheidung führt dazu, dass der die Datenorganisation und der Datenfluss explizit behandelt werden muss, was die Möglichkeiten für eine automatische und autonome Optimierung des Systems einschränkt. Der in dieser Arbeit vorgestellte Ansatz zur Skalierung und Allokation erhält den transparenten Zugriff und zeichnet sich dabei durch seine vollständige Automatisierbarkeit und sehr gute Skalierbarkeit aus. Im ersten Teil dieser Dissertation werden die Herausforderungen und Chancen für selbst-skalierende Datenbankmanagementsysteme behandelt, die in auf Computerclustern betrieben werden. Gute Skalierbarkeit ist eine notwendige Eigenschaft für Anwendungen, die über das Internet zugreifbar sind. Lastspitzen im Zugriff, die die Anwendung überladen stellen ein finanzielles Risiko dar. Deshalb werden Systeme so konfiguriert, dass sie eventuelle Lastspitzen zu jedem Zeitpunkt verarbeiten können. Das führt meist zu einer im Schnitt sehr geringen Auslastung der unterliegenden Systeme. Eine Möglichkeit dieser Ineffizienz entgegen zu steuern ist es die Anzahl der verwendeten Rechnerknoten an die vorliegende Last anzupassen. In dieser Dissertation werden ein Modell und eine Architektur für die Anfrageverarbeitung vorgestellt, mit denen es möglich ist Datenbanksysteme auf Clusterrechnern einfach und effizient zu skalieren. Im zweiten Teil der Arbeit werden verschieden Möglichkeiten für die Datenverteilung behandelt. Um die Effizienz zu steigern wird ein Modell verwendet, das die Lastverteilung im Anfragestrom berücksichtigt. Der Ansatz ist formalisiert und optimale und heuristische Lösungen werden präsentiert. Die vorgestellten Algorithmen optimieren die Datenverteilung für eine lokale Ausführung aller Anfragen und balancieren die Last auf den Rechnerknoten. Es werden unterschiedliche Arten der Anfrageklassifizierung vorgestellt, die zu verschiedenen Arten von Partitionierung führen. Der Ansatz wird sowohl für Onlinetransaktionsverarbeitung, als auch Onlinedatenanalyse evaluiert. Die Evaluierung zeigt, dass der Ansatz für beide Felder sehr gut skaliert. Im letzten Teil der Arbeit werden verschiedene Techniken für die Leistungsmessung von großen, adaptiven Systemen präsentiert. Zunächst wird ein Datengenerierungsansatz gezeigt, der es ermöglicht sehr große Datenmengen völlig parallel zu erzeugen. Um die Benutzerinteraktion von Onlinesystemen zu simulieren wurde ein produktives E-learningsystem analysiert. Anhand der Analyse wurde ein Modell für die Generierung von Arbeitslasten erstellt, das die zeitlichen Abhängigkeiten von Benutzerinteraktion berücksichtigt

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

Faculty Publications and Creative Works 2005

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM. In 2005, UNM faculty produced over 1,887 works, including 1,887 scholarly papers and articles, 57 books, 127 book chapters, 58 reviews, 68 creative works and 4 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico