Distributed computing for the factory-floor: a real-time approach using WorldFIP networks

Field communication systems (fieldbuses) are widely used as the communication support for distributed computer-controlled systems (DCCS) within all sort of process control and manufacturing applications. There are several advantages in the use of fieldbuses as a replacement for the traditional point-to-point links between sensors/actuators and computer-based control systems, within which the most relevant is the decentralisation and distribution of the processing power over the field. A widely used fieldbus is the WorldFIP, which is normalised as European standard EN 50170. Using WorldFIP to support DCCS, an important issue is “how to guarantee the timing requirements of the real-time traffic?” WorldFIP has very interesting mechanisms to schedule data transfers, since it explicitly distinguishes periodic and aperiodic traffic. In this paper, we describe how WorldFIP handles these two types of traffic, and more importantly, we provide a comprehensive analysis on how to guarantee the timing requirements of the real-time traffic.

Autonomic workflow activities: the award framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.

Mobile collaborative cloudless computing

Although the computational power of mobile devices has been increasing, it is still not enough for some classes of applications. In the present, these applications delegate the computing power burden on servers located on the Internet. This model assumes an always-on Internet connectivity and implies a non-negligible latency. The thesis addresses the challenges and contributions posed to the application of a mobile collaborative computing environment concept to wireless networks. The goal is to define a reference architecture for high performance mobile applications. Current work is focused on efficient data dissemination on a highly transitive environment, suitable to many mobile applications and also to the reputation and incentive system available on this mobile collaborative computing environment. For this we are improving our already published reputation/incentive algorithm with knowledge from the usage pattern from the eduroam wireless network in the Lisbon area.

BITalino: a novel hardware framework for physiological computing

Physical computing has spun a true global revolution in the way in which the digital interfaces with the real world. From bicycle jackets with turn signal lights to twitter-controlled christmas trees, the Do-it-Yourself (DiY) hardware movement has been driving endless innovations and stimulating an age of creative engineering. This ongoing (r)evolution has been led by popular electronics platforms such as the Arduino, the Lilypad, or the Raspberry Pi, however, these are not designed taking into account the specific requirements of biosignal acquisition. To date, the physiological computing community has been severely lacking a parallel to that found in the DiY electronics realm, especially in what concerns suitable hardware frameworks. In this paper, we build on previous work developed within our group, focusing on an all-in-one, low-cost, and modular biosignal acquisition hardware platform, that makes it quicker and easier to build biomedical devices. We describe the main design considerations, experimental evaluation and circuit characterization results, together with the results from a usability study performed with volunteers from multiple target user groups, namely health sciences and electrical, biomedical, and computer engineering. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

Trends Of CPU, GPU and FPGA for high-performance computing

Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).

A contribuição da informática para o desenvolvimento sustentável: o exemplo do conceito de grid computing

Este trabalho foi realizado sob orientação do Prof. António Brandão Moniz para a disciplina “Factores Sociais da Inovação” do Mestrado Engenharia Informática realizado na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (Portugal)

Optimization of structures modeled with a Meshfree approach

Radial basis functions are being used in different scientific areas in order to reproduce the geometrical modeling of an object/structure, as well as to predict its behavior. Due to its characteristics, these functions are well suited for meshfree modeling of physical quantities, which for instances can be associated to the data sets of 3D laser scanning point clouds. In the present work the geometry of a structure is modeled by using multiquadric radial basis functions, and its configuration is further optimized in order to obtain better performances concerning to its static and dynamic behavior. For this purpose the authors consider the particle swarm optimization technique. A set of case studies is presented to illustrate the adequacy of the meshfree model used, as well as its link to particle swarm optimization technique. © 2014 IEEE.

Ubiquitous computing and natural interfaces for environmental information

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas Ambientais

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

Reliability and availability in reconfigurable computing: a basis for a common solution

Dynamically reconfigurable SRAM-based field-programmable gate arrays (FPGAs) enable the implementation of reconfigurable computing systems where several applications may be run simultaneously, sharing the available resources according to their own immediate functional requirements. To exclude malfunctioning due to faulty elements, the reliability of all FPGA resources must be guaranteed. Since resource allocation takes place asynchronously, an online structural test scheme is the only way of ensuring reliable system operation. On the other hand, this test scheme should not disturb the operation of the circuit, otherwise availability would be compromised. System performance is also influenced by the efficiency of the management strategies that must be able to dynamically allocate enough resources when requested by each application. As those resources are allocated and later released, many small free resource blocks are created, which are left unused due to performance and routing restrictions. To avoid wasting logic resources, the FPGA logic space must be defragmented regularly. This paper presents a non-intrusive active replication procedure that supports the proposed test methodology and the implementation of defragmentation strategies, assuring both the availability of resources and their perfect working condition, without disturbing system operation.

Meios de partilha de recursos educativos digitais: LMS, CMS e Cloud Computing

Para muitos, o ato de ensinar, era e continua a ser uma “arte”, em que os professores e os grandes mestres mais eficientes são aqueles que têm a capacidade e a arte de fazer passar as suas mensagens e conhecimentos, de forma simples e apelativa, independentemente da área de estudo. A informação relacionada com a aula, é cada vez mais digital, sendo importante, por parte dos docentes, o domínio de tecnologias de criação, organização e disponibilização de conteúdos. Essa partilha foi inicialmente possível pelas páginas Web e mais tarde pelas plataformas LMS (Learning Management System). Criar um Website era uma tarefa complicada quer ao nível do seu custo quer ao nível do domínio da tecnologia Web e era por vezes necessário contratar profissionais para o efeito. Surgiram então as CMS (Content Management System) que são tecnologias Open Source, que permitem a gestão de conteúdos. Neste sentido foi realizado um estudo com o objetivo de aferir sobre as competências dos professores no domínio da partilha de Gestão de Conteúdos Digitais. O presente estudo permitiu retirar conclusões sobre o potencial e aplicabilidade das CMS no ensino. O principal objetivo do presente estudo incidiu no potencial de distribuição e partilha de Recursos Educativos Digitais organizados sobre o ponto de vista pedagógico aos alunos. Foi ainda analisado e estudado o papel do Cloud Computing no processo de partilha colaborativa de documentos. Foi delineado como suporte à presente investigação um curso modelo que por sua vez foi implementado nas três principais CMS da atualidade e avaliado o potencial de cada uma neste contexto. Finalmente foram apresentadas as conclusões retiradas do presente estudo.

Estimating Effective Slowdown of Tasks in Energy-Aware Clouds

Consolidation consists in scheduling multiple virtual machines onto fewer servers in order to improve resource utilization and to reduce operational costs due to power consumption. However, virtualization technologies do not offer performance isolation, causing applications’ slowdown. In this work, we propose a performance enforcing mechanism, composed of a slowdown estimator, and a interference- and power-aware scheduling algorithm. The slowdown estimator determines, based on noisy slowdown data samples obtained from state-of-the-art slowdown meters, if tasks will complete within their deadlines, invoking the scheduling algorithm if needed. When invoked, the scheduling algorithm builds performance and power aware virtual clusters to successfully execute the tasks. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our strategy can be efficiently integrated with state-of-the-art slowdown meters to fulfil contracted SLAs in real-world environments, while reducing operational costs in about 12%.

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper.