Towards the design of an industrial autonomic network node

Abstract. Programmable and active networks allow specified classes of users to deploy dynamic network services adapted to data streams requirements. Currently most of researches performed on active networks are conducted in research laboratories. In this paper, we explore the design of IAN 2 an Industrial Autonomic Network Node able to be deployed in industrial context. Performance, dynamic programmability and fault-tolerance issues of software and hardware components have been prospected. First experimental evaluations on local platforms are presented

Traffic and task allocation in networks and the cloud

Communication services such as telephony, broadband and TV are increasingly migrating into Internet Protocol(IP) based networks because of the consolidation of telephone and data networks. Meanwhile, the increasingly wide application of Cloud Computing enables the accommodation of tens of thousands of applications from the general public or enterprise users which make use of Cloud services on-demand through IP networks such as the Internet. Real-Time services over IP (RTIP) have also been increasingly significant due to the convergence of network services, and the real-time needs of the Internet of Things (IoT) will strengthen this trend. Such Real-Time applications have strict Quality of Service (QoS) constraints, posing a major challenge for IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement. Thus in this thesis we first describe our design for a novel ``Real-Time (RT) traffic over CPN'' protocol which uses QoS goals that match the needs of voice packet delivery in the presence of other background traffic under varied traffic conditions; we present its experimental evaluation via measurements of key QoS metrics such as packet delay, delay variation (jitter) and packet loss ratio. Pursuing our investigation of packet routing in the Internet, we then propose a novel Big Data and Machine Learning approach for real-time Internet scale Route Optimisation based on Quality-of-Service using an overlay network, and evaluate is performance. Based on the collection of data sampled each $2$ minutes over a large number of source-destinations pairs, we observe that intercontinental Internet Protocol (IP) paths are far from optimal with respect to metrics such as end-to-end round-trip delay. On the other hand, our machine learning based overlay network routing scheme exploits large scale data collected from communicating node pairs to select overlay paths, while it uses IP between neighbouring overlay nodes. We report measurements over a week long experiment with several million data points shows substantially better end-to-end QoS than is observed with pure IP routing. Pursuing the machine learning approach, we then address the challenging problem of dispatching incoming tasks to servers in Cloud systems so as to offer the best QoS and reliable job execution; an experimental system (the Task Allocation Platform) that we have developed is presented and used to compare several task allocation schemes, including a model driven algorithm, a reinforcement learning based scheme, and a ``sensible’’ allocation algorithm that assigns tasks to sub-systems that are observed to provide lower response time. These schemes are compared via measurements both among themselves and against a standard round-robin scheduler, with two architectures (with homogenous and heterogenous hosts having different processing capacities) and the conditions under which the different schemes offer better QoS are discussed. Since Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet, we also describe a smart distributed system that combines local and remote Cloud facilities, allocating tasks dynamically to the service that offers the best overall QoS, and it includes a routing overlay which minimizes network delay for data transfer between Clouds. Internet-scale experiments that we report exhibit the effectiveness of our approach in adaptively distributing workload across multiple Clouds.Open Acces

Dynamic Upgrade of Distributed Software Components

Die Aktualisierung von komplexen Telekommunikationssystemen, die sich durch die ihnen eigene Verteiltheit und hohe Kosten bei System-Nichtverfügbarkeit auszeichnen, ist ein komplizierter und fehleranfälliger Wartungsprozess. Noch stärkere Herausforderungen bergen solche Software-Aktualisierungen, die die Systemverfügbarkeit nicht beeinträchtigen sollen. Dynamic Upgrade ist eine Wartungstechnik, die das Verwalten und die Durchführung von Software-Aktualisierung automatisiert und damit den Betrieb des Systems während der Wartungszeit nicht unterbricht. In dieser Arbeit wird das Dynamic Upgrade als ein Sonderfall der Bereitstellung und Inbetriebnahme (Deployment) von Software betrachtet, in dem Teile der einen Dienst repräsentierenden Software durch neue Versionen im laufenden Betrieb ersetzt werden. Die Problemstellung des Dynamic Upgrade wird anhand einer vom Autor erarbeiteten Taxonomie erläutert, die die Entwurfsmöglichkeiten für ein System zur Unterstützung von Dynamic Upgrade hinsichtlich dreier Systemaspekte klassifiziert: Deployment, Evolution und Zuverlässigkeit (Dependability). Mit Hilfe dieser Taxonomie lassen sich auch andere Systeme zur Unterstützung von Dynamic Upgrade miteinander vergleichen. Aufbauend auf einem ausführlichen Vergleich über existierende Ansätze zur Unterstützung von Dynamic Upgrade, wird in der vorliegenden Arbeit eine Lösung entwickelt und dargestellt, die Dynamic Upgrade in verteilten komponentenbasierten Software-Systemen ermöglicht. Ausgehend von der Problemanalyse wird mit Hilfe des Unified Process ein als Deployment and Upgrade Facility bezeichnetes Modell entwickelt, das sowohl die benötigten Leistungsfähigkeiten eines Dynamic Upgrade unterstützenden Systems als auch Eigenschaften von aktualisierbaren Software-Komponenten beschreibt. Dieses Modell ist Plattform-unabhängig und einsetzbar für mehrere unterliegende Middleware-Technologien. Das Modell wird in einem Java-basierten prototypischen Rahmenwerk programmiert und um plattformspezifische Mechanismen auf der Jgroup/ARM Middleware erweitert. Das Rahmenwerk umfasst allgemeine Entwurfslösungen und ?muster, die sich für die Konstruktion einer Unterstützung für Dynamic Upgrade eignen. Es erlaubt die Kontrolle der Lebenszyklen von Aktualisierungsprozessen und ihre Koordination im Zielsystem. Darüber hinaus definiert es eine Reihe von Unterstützungsmechanismen und Algorithmen für den dynamischen Aktualisierungsprozess, der gegebenenfalls mit unterschiedlichen Zielsetzungen und unter verschiedenen Randbedingungen erfolgen soll. Insbesondere wird ein Aktualisierungsalgorithmus für replizierte Software-Komponenten dargestellt. Das entwickelte Rahmenwerk wird zwecks Plausibilitätsprüfung der dargestellten Ansätze und zur Auswertung der Auswirkungen der Dynamic Upgrade unterstützenden Mechanismen im Hinblick auf Systemperformanz in mehreren Experimenten eingesetzt. Diese quantitative Evaluierung der Experimente führt zu einer Spezifikationen eines einfachen Bewertungsmaßstabs (Benchmark), der sich zum Vergleich von Dynamic Upgrade unterstützenden Systemen eignet.Upgrading complex telecommunication software systems, characterized by their inherent distribution and a very high cost of system unavailability, is a difficult and error-prone maintenance activity. Even more challenging are such software upgrades that do not compromise the system availability. Dynamic upgrades is a technique, which automates performing and managing upgrades so that the software system remains operational during the upgrade time. In this thesis, the dynamic upgrade is considered as a special case of software deployment, in which a running service has to be replaced with its new version. The problems of dynamic upgrades are introduced using a novel taxonomy that classifies the design issues to be solved when building support for dynamic upgrade with regard to three system aspects: deployment, evolution and dependability and provides a reference to comparing other systems supporting dynamic upgrades. An extensive and thorough survey of existing approaches to dynamic upgrades follows and, furthermore, is as a starting point to designing a solution supporting dynamic upgrades in distributed component-based software systems. Derived from the problem analysis, a model called Deployment and Upgrade Facility describing the capabilities needed for managing and performing dynamic upgrades as well as properties of upgradable software components is developed using the Unified Process approach. The model is platform independent and can be used with a range of underlying middleware technologies. The model is implemented in a Java-based prototypical framework and extended with platform specific mechanisms on top of the JGroup/ARM middleware. The framework captures common design solutions and patterns for building a support for dynamic upgrade. The framework allows for controlling life-cycle and coordination of upgrade processes in the system. It also defines a number of supporting mechanisms and algorithms for the upgrade process. A special attention is drawn to an upgrade algorithm for replicated software components for achieving a synergy of replication techniques and dynamic upgrade . The developed framework is used to validate the feasibility of the approach and to measure the overhead of the mechanisms supporting dynamic upgrade with regard to the performance of the system being upgraded in a number of practical experiments. This quantitative evaluation of the experiments leads to a specification of a simple benchmark for systems supporting dynamic upgrades

Filtrado de respuestas parciales en arquitecturas de recuperación de información distribuidas

[Resumen] El gran incremento del volumen de información disponible en línea desde hace unas décadas hace necesarias, cada vez más, técnicas de recuperación de información con el objetivo de gestionar, recuperar y filtrar la información disponible por estos medios, Las líneas de investigación actuales han descubierto la existencia de un cuello de botella en el canal de respuesta de las arquitecturas de recuperación de información distribuida, debido, principalmente, al gran número de respuestas parciales y su consiguiente influencia sobre las arquitecturas de comunicaciones y sobre los brokers de usuario. Por un lado, si disminuimos el número de respuestas parciales generados por cada servidor de consulta, disminuimos la precisión de la respuesta final; por el otro lado, si diseñamos una arquitectura de comunicaciones que soporte el tráfico generado obtenemos un claro cuello de botella al ordenar los resultados parciales en los brokers de usuario con el fin de seleccionar la respuesta final. La solución que propone este trabajo consiste en una arquitectura filtrada de comunicaciones implementada mediante nodos programables, los cuales forman una subred que posee la capacidad de procesar de forma transparente el tráfico que la atraviesa, con el objetivo de reducir la cardinalidad de las respuestas parciales. Así pues la principal ventaja de usar nodos programables en vez de, por ejemplo, brokers es la transparencia; lo que permite construir infraestructuras de canales de respuesta altamente flexibles y facilmetne modificables en tiempo de ejecución