Topics in Power Usage in Network Services

The rapid advance of computing technology has created a world powered by millions of computers. Often these computers are idly consuming energy unnecessarily in spite of all the efforts of hardware manufacturers. This thesis examines proposals to determine when to power down computers without negatively impacting on the service they are used to deliver, compares and contrasts the efficiency of virtualisation with containerisation, and investigates the energy efficiency of the popular cryptocurrency Bitcoin. We begin by examining the current corpus of literature and defining the key terms we need to proceed. Then we propose a technique for improving the energy consumption of servers by moving them into a sleep state and employing a low powered device to act as a proxy in its place. After this we move on to investigate the energy efficiency of virtualisation and compare the energy efficiency of two of the most common means used to do this. Moving on from this we look at the cryptocurrency Bitcoin. We consider the energy consumption of bitcoin mining and if this compared with the value of bitcoin makes this profitable. Finally we conclude by summarising the results and findings of this thesis. This work increases our understanding of some of the challenges of energy efficient computation as well as proposing novel mechanisms to save energy

Image recognition with Deep Learning techniques and TensorFlow

Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide range of application, but most notoriously in computer vision and natural language processing tasks. Despite the newly found interest, research in neural networks span many decades back, and some of today’s most used network architectures where invented many years ago. Nevertheless, the progress made during this period cannot be understood without taking into account the technological advancements seen in key contiguous domains such as massive data storage and computing systems, more specifically in the Graphic Processing Unit (GPU) domain. These two components are responsible for the enormous performance gains in neural networks, that have made what we call Deep Learning a common word among the Artificial Intelligence and Machine Learning community. These kind of networks need massive amounts of data to effectively train the millions of parameters they contain, and this training can take up to days or weeks depending on the computer architecture we are using. The size of new published datasets keeps growing, and the tendency of creating deeper networks that outperforms shallower architectures means that on the medium and long term the computer hardware to undertake these kind of training processes can only be found in high performance computing facilities, where they have enormous clusters of computers. However, using these machines is not straightforward, as both the framework and the code need to be appropriately tuned for effectively taking advantage of these distributed environments. For this reason, we test TensorFlow, an open-sourced framework for Deep Learning from Google that has built-in distributed support, on top of the GPU cluster, called MinoTauro, at Barcelona Supercomputing Center (BSC). We aim to implement a defined workload using the distributed features the framework offers, to speed up the training process, acquire knowledge of the inner workings of the framework and understand the similarities and differences with respect to a classic single node training

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Secure Communication in Disaster Scenarios

Während Naturkatastrophen oder terroristischer Anschläge ist die bestehende Kommunikationsinfrastruktur häufig überlastet oder fällt komplett aus. In diesen Situationen können mobile Geräte mithilfe von drahtloser ad-hoc- und unterbrechungstoleranter Vernetzung miteinander verbunden werden, um ein Notfall-Kommunikationssystem für Zivilisten und Rettungsdienste einzurichten. Falls verfügbar, kann eine Verbindung zu Cloud-Diensten im Internet eine wertvolle Hilfe im Krisen- und Katastrophenmanagement sein. Solche Kommunikationssysteme bergen jedoch ernsthafte Sicherheitsrisiken, da Angreifer versuchen könnten, vertrauliche Daten zu stehlen, gefälschte Benachrichtigungen von Notfalldiensten einzuspeisen oder Denial-of-Service (DoS) Angriffe durchzuführen. Diese Dissertation schlägt neue Ansätze zur Kommunikation in Notfallnetzen von mobilen Geräten vor, die von der Kommunikation zwischen Mobilfunkgeräten bis zu Cloud-Diensten auf Servern im Internet reichen. Durch die Nutzung dieser Ansätze werden die Sicherheit der Geräte-zu-Geräte-Kommunikation, die Sicherheit von Notfall-Apps auf mobilen Geräten und die Sicherheit von Server-Systemen für Cloud-Dienste verbessert

Hardware Acceleration of Network Intrusion Detection System Using FPGA

This thesis presents new algorithms and hardware designs for Signature-based Network Intrusion Detection System (SB-NIDS) optimisation exploiting a hybrid hardwaresoftware co-designed embedded processing platform. The work describe concentrates on optimisation of a complete SB-NIDS Snort application software on a FPGA based hardware-software target rather than on the implementation of a single functional unit for hardware acceleration. Pattern Matching Hardware Accelerator (PMHA) based on Bloom filter was designed to optimise SB-NIDS performance for execution on a Xilinx MicroBlaze soft-core processor. The Bloom filter approach enables the potentially large number of network intrusion attack patterns to be efficiently represented and searched primarily using accesses to FPGA on-chip memory. The thesis demonstrates, the viability of hybrid hardware-software co-designed approach for SB-NIDS. Future work is required to investigate the effects of later generation FPGA technology and multi-core processors in order to clearly prove the benefits over conventional processor platforms for SB-NIDS. The strengths and weaknesses of the hardware accelerators and algorithms are analysed, and experimental results are examined to determine the effectiveness of the implementation. Experimental results confirm that the PMHA is capable of performing network packet analysis for gigabit rate network traffic. Experimental test results indicate that our SB-NIDS prototype implementation on relatively low clock rate embedded processing platform performance is approximately 1.7 times better than Snort executing on a general purpose processor on PC when comparing processor cycles rather than wall clock time

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation