Upnp-Based Discovery And Management Of Hypervisors And Virtual Machines

The paper introduces a Universal Plug and Play based discovery and management toolkitthat facilitates collaboration between cloud infrastructure providers and users. The presentedtools construct a unified hierarchy of devices and their management-related services, thatrepresents the current deployment of users’ (virtual) infrastructures in the provider’s (physical)infrastructure as well as the management interfaces of respective devices. The hierarchycan be used to enhance the capabilities of the provider’s infrastructure management system.To maintain user independence, the set of management operations exposed by a particulardevice is always defined by the device owner (either the provider or user)

Building real-time audio applications with component technology

The thesis looks at how QoS-aware applications (QSAs), with an Internet phone as a case, can be built from components (with Enterprise Java Beans (EJB) as the chosen component model). It also identifies limitations of the EJB standard that cause problems implementing real-time audio applications. The requirements to the EJB platform are identified by analyzing a generic design which is made from common components. These components are found by inspecting existing applications and communication architectures. Three Internet phone applications (with and without EJB) are made for performance measurements, and as a help to the analysis work. Finally in this thesis, a list of suggested extensions/changes to the EJB standard that can eliminate the identified limitations, is presente

IRC channel data analysis using Apache Solr

Internet Relay Chat (IRC) was one of the first real-time communication protocols over the internet. It was not designed with any form of Authentication, Authorization and Accounting features. This made IRC channels a place to conduct transactions in complete anonymity. On the other hand with the advent of Big Data we are now able to process large quantities of data in a very short period of time. This research presents a method to use Apache Solr, a text indexing server built on top of Lucene to index and search large quantities of IRC data collected over months from public IRC channels. It even presents a highly scalable approach to monitor public IRC channels by creation of IRC Client Bots which are in turn controlled by a robust IRC Parent Bot. The data thus collected is analyzed by Apache Solr and MS SQL servers and the response times are compared. This research concluded that Apache Solr outperforms MS SQL by a very great margin and such an implementation can be used by digital forensic investigators to monitor and search public IRC channels

Analyse des performances de stockage, en mémoire et sur les périphériques d'entrée/sortie, à partir d'une trace d'exécution

Le stockage des données est vital pour l’industrie informatique. Les supports de stockage doivent être rapides et fiables pour répondre aux demandes croissantes des entreprises. Les technologies de stockage peuvent être classifiées en deux catégories principales : stockage de masse et stockage en mémoire. Le stockage de masse permet de sauvegarder une grande quantité de données à long terme. Les données sont enregistrées localement sur des périphériques d’entrée/sortie, comme les disques durs (HDD) et les Solid-State Drive (SSD), ou en ligne sur des systèmes de stockage distribué. Le stockage en mémoire permet de garder temporairement les données nécessaires pour les programmes en cours d’exécution. La mémoire vive est caractérisée par sa rapidité d’accès, indispensable pour fournir rapidement les données à l’unité de calcul du processeur. Les systèmes d’exploitation utilisent plusieurs mécanismes pour gérer les périphériques de stockage, par exemple les ordonnanceurs de disque et les allocateurs de mémoire. Le temps de traitement d’une requête de stockage est affecté par l’interaction entre plusieurs soussystèmes, ce qui complique la tâche de débogage. Les outils existants, comme les outils d’étalonnage, permettent de donner une vague idée sur la performance globale du système, mais ne permettent pas d’identifier précisément les causes d’une mauvaise performance. L’analyse dynamique par trace d’exécution est très utile pour l’étude de performance des systèmes. Le traçage permet de collecter des données précises sur le fonctionnement du système, ce qui permet de détecter des problèmes de performance difficilement identifiables. L’objectif de cette thèse est de fournir un outil permettant d’analyser les performances de stockage, en mémoire et sur les périphériques d’entrée/sortie, en se basant sur les traces d’exécution. Les défis relevés par cet outil sont : collecter les données nécessaires à l’analyse depuis le noyau et les programmes en mode utilisateur, limiter le surcoût du traçage et la taille des traces générées, synchroniser les différentes traces, fournir des analyses multiniveau couvrant plusieurs aspects de la performance et enfin proposer des abstractions permettant aux utilisateurs de facilement comprendre les traces.----------ABSTRACT: Data storage is an essential resource for the computer industry. Storage devices must be fast and reliable to meet the growing demands of the data-driven economy. Storage technologies can be classified into two main categories: mass storage and main memory storage. Mass storage can store large amounts of data persistently. Data is saved locally on input/output devices, such as Hard Disk Drives (HDD) and Solid-State Drives (SSD), or remotely on distributed storage systems. Main memory storage temporarily holds the necessary data for running programs. Main memory is characterized by its high access speed, essential to quickly provide data to the Central Processing Unit (CPU). Operating systems use several mechanisms to manage storage devices, such as disk schedulers and memory allocators. The processing time of a storage request is affected by the interaction between several subsystems, which complicates the debugging task. Existing tools, such as benchmarking tools, provide a general idea of the overall system performance, but do not accurately identify the causes of poor performance. Dynamic analysis through execution tracing is a solution for the detailed runtime analysis of storage systems. Tracing collects precise data about the internal behavior of the system, which helps in detecting performance problems that are difficult to identify. The goal of this thesis is to provide a tool to analyze storage performance based on lowlevel trace events. The main challenges addressed by this tool are: collecting the required data using kernel and userspace tracing, limiting the overhead of tracing and the size of the generated traces, synchronizing the traces collected from different sources, providing multi-level analyses covering several aspects of storage performance, and lastly proposing abstractions allowing users to easily understand the traces. We carefully designed and inserted the instrumentation needed for the analyses. The tracepoints provide full visibility into the system and track the lifecycle of storage requests, from creation to processing. The Linux Trace Toolkit Next Generation (LTTng), a free and low-overhead tracer, is used for data collection. This tracer is characterized by its stability, and efficiency with highly parallel applications, thanks to the lock-free synchronization mechanisms used to update the content of the trace buffers. We also contributed to the creation of a patch that allows LTTng to capture the call stacks of userspace events

SCA Platform Specifications - Version 2.0

The SCOrWare project aims at building an open source implementation of the Service Component Architecture (SCA) specifications. This implementation is composed of 1) a runtime platform for deploying, executing, and managing SCAbased applications, 2) a set of development tools for modeling, designing, and implementing SCA-based applications, and 3) a set of demonstrators. This document contains the specifications of the SCOrWare runtime platform. Section 1.1 lists the parts of SCA specifications supported by the SCOrWare platform. Section 1.2 gives an overview of the SCOrWare platform, and a summary of next chapters of this document. Section 1.3 lists the main update from the version 1 to the version 2 of this documen

The architecture of discovery net : towards grid-based discovery services

Imperial Users onl