A parallel programming model for ada

Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, parallel programming models are proposed as promising solutions for more effectively using multi-core processors. This paper discusses some of the existent models and frameworks for parallel programming, leading to outline a draft parallel programming model for Ada.

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.

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to perform services with specific Quality of Service constraints, particularly in dynamic distributed environments where the characteristics of the computational load cannot always be predicted in advance. Our work addresses this problem by allowing resource constrained devices to cooperate with more powerful neighbour nodes, opportunistically taking advantage of global distributed resources and processing power. Rather than assuming that the dynamic configuration of this cooperative service executes until it computes its optimal output, the paper proposes an anytime approach that has the ability to tradeoff deliberation time for the quality of the solution. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves at each iteration, with an overhead that can be considered negligible.

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.

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)

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

Parallel texts alignment

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

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.

Autonomic Activities in the Execution of Scientific Workflows: Evaluation of the AWARD Framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from tasks specification, decentralizing the control of workflow activities allowing their tasks to run in distributed infrastructures, and supporting dynamic workflow reconfigurations. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on Process Networks, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures. Each AWA executes a task developed as a Java class with a generic interface allowing end-users to code their applications without low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables dynamic workflow reconfiguration. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to the Amazon (Elastic Computing EC2) Cloud.

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.

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.

Optimization of multi-stage amplifiers in deep-submicron CMOS using a distributed/parallel genetic algorithm

IEEE International Symposium on Circuits and Systems, pp. 724 – 727, Seattle, EUA

Integrated Management of IaaS Resources

This paper proposes and reports the development of an open source solution for the integrated management of Infrastructure as a Service (IaaS) cloud computing resources, through the use of a common API taxonomy, to incorporate open source and proprietary platforms. This research included two surveys on open source IaaS platforms (OpenNebula, OpenStack and CloudStack) and a proprietary platform (Parallels Automation for Cloud Infrastructure - PACI) as well as on IaaS abstraction solutions (jClouds, Libcloud and Deltacloud), followed by a thorough comparison to determine the best approach. The adopted implementation reuses the Apache Deltacloud open source abstraction framework, which relies on the development of software driver modules to interface with different IaaS platforms, and involved the development of a new Deltacloud driver for PACI. The resulting interoperable solution successfully incorporates OpenNebula, OpenStack (reuses pre-existing drivers) and PACI (includes the developed Deltacloud PACI driver) nodes and provides a Web dashboard and a Representational State Transfer (REST) interface library. The results of the exchanged data payload and time response tests performed are presented and discussed. The conclusions show that open source abstraction tools like Deltacloud allow the modular and integrated management of IaaS platforms (open source and proprietary), introduce relevant time and negligible data overheads and, as a result, can be adopted by Small and Medium-sized Enterprise (SME) cloud providers to circumvent the vendor lock-in problem whenever service response time is not critical.