Compositional multiprocessor scheduling: the GMPR interface

Composition is a practice of key importance in software engineering. When real-time applications are composed, it is necessary that their timing properties (such as meeting the deadlines) are guaranteed. The composition is performed by establishing an interface between the application and the physical platform. Such an interface typically contains information about the amount of computing capacity needed by the application. For multiprocessor platforms, the interface should also present information about the degree of parallelism. Several interface proposals have recently been put forward in various research works. However, those interfaces are either too complex to be handled or too pessimistic. In this paper we propose the generalized multiprocessor periodic resource model (GMPR) that is strictly superior to the MPR model without requiring a too detailed description. We then derive a method to compute the interface from the application specification. This method has been implemented in Matlab routines that are publicly available.

Real-time scheduling with resource sharing on heterogeneous multiprocessors

Consider the problem of scheduling a task set τ of implicit-deadline sporadic tasks to meet all deadlines on a t-type heterogeneous multiprocessor platform where tasks may access multiple shared resources. The multiprocessor platform has m k processors of type-k, where k∈{1,2,…,t}. The execution time of a task depends on the type of processor on which it executes. The set of shared resources is denoted by R. For each task τ i , there is a resource set R i ⊆R such that for each job of τ i , during one phase of its execution, the job requests to hold the resource set R i exclusively with the interpretation that (i) the job makes a single request to hold all the resources in the resource set R i and (ii) at all times, when a job of τ i holds R i , no other job holds any resource in R i . Each job of task τ i may request the resource set R i at most once during its execution. A job is allowed to migrate when it requests a resource set and when it releases the resource set but a job is not allowed to migrate at other times. Our goal is to design a scheduling algorithm for this problem and prove its performance. We propose an algorithm, LP-EE-vpr, which offers the guarantee that if an implicit-deadline sporadic task set is schedulable on a t-type heterogeneous multiprocessor platform by an optimal scheduling algorithm that allows a job to migrate only when it requests or releases a resource set, then our algorithm also meets the deadlines with the same restriction on job migration, if given processors 4×(1+MAXP×⌈|P|×MAXPmin{m1,m2,…,mt}⌉) times as fast. (Here MAXP and |P| are computed based on the resource sets that tasks request.) For the special case that each task requests at most one resource, the bound of LP-EE-vpr collapses to 4×(1+⌈|R|min{m1,m2,…,mt}⌉). To the best of our knowledge, LP-EE-vpr is the first algorithm with proven performance guarantee for real-time scheduling of sporadic tasks with resource sharing on t-type heterogeneous multiprocessors.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

A New Heuristic Providing an Effective Initial Solution for a Simulated Annealing approach to Energy Resource Scheduling in Smart Grids

An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.

Hard real-time multiprocessor scheduling resilient to core failures

Presented at 21st IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2015). 19 to 21, Aug, 2015, pp 122-131. Hong Kong, China.

Is mental practice an effective adjunct therapeutic strategy for upper limb motor restoration after stroke? A systematic review and meta- analysis

Stroke is one of the most common conditions requiring rehabilitation, and its motor impairments are a major cause of permanent disability. Hemiparesis is observed by 80% of the patients after acute stroke. Neuroimaging studies showed that real and imagined movements have similarities regarding brain activation, supplying evidence that those similarities are based on the same process. Within this context, the combination of mental practice (MP) with physical and occupational therapy appears to be a natural complement based on neurorehabilitation concepts. Our study seeks to investigate if MP for stroke rehabilitation of upper limbs is an effective adjunct therapy. PubMed (Medline), ISI knowledge (Institute for Scientific Information) and SciELO (Scientific Electronic Library) were terminated on 20 February 2015. Data were collected on variables as follows: sample size, type of supervision, configuration of mental practice, setting the physical practice (intensity, number of sets and repetitions, duration of contractions, rest interval between sets, weekly and total duration), measures of sensorimotor deficits used in the main studies and significant results. Random effects models were used that take into account the variance within and between studies. Seven articles were selected. As there was no statistically significant difference between the two groups (MP vs control), showed a - 0.6 (95% CI: -1.27 to 0.04), for upper limb motor restoration after stroke. The present meta-analysis concluded that MP is not effective as adjunct therapeutic strategy for upper limb motor restoration after stroke.

From Mind to Body: Is Mental Practice Effective on Strength Gains? A Meta-Analysis

Mental practice is an internal reproduction of a motor act (whose intention is to promote learning and improving motor skills). Some studies have shown that other cognitive strategies also increase the strength and muscular resistance in healthy people by the enhancement of the performance during dynamic tasks. Mental training sessions may be primordial to improving muscle strength in different subjects. The aim of this study was to systematically review and meta-analiyze studies that assessed whether mental practice is effective in improving muscular strength. We conducted an electronic-computed search in Pub-Med/Medline and ISI Web of Knowledge, Scielo and manual searchs, searching papers written in English between 1991 and 2014. There were 44 studies in Pub-Med/Medline, 631 in ISI Web of Knowledge, 11 in Scielo and 3 in manual searchs databases. After exclusion of studies for duplicate, unrelated to the topic by title and summary, different samples and methodologies, a meta-analysis of 4 studies was carried out to identify the dose-response relationship. We did not find evidence that mental practice is effective in increasing strength in healthy individuals. There is no evidence that mental practice alone can be effective to induce strength gains or to optimize the training effects.

O impacto do sono no desempenho de tarefas de memória procedimental em crianças: Revisão sistemática e meta-análise

Introdução: Estudos actuais sugerem que o sono possui um papel importante na melhoria do desempenho motor em adultos. Porém, em crianças saudáveis os resultados são contraditórios, indicando que apenas a passagem do tempo pode produzir um ganho ilusório. Objetivo: Identificar e sumarizar a evidência acerca do impacto do sono no desempenho de tarefas de memória procedimental em crianças saudáveis. Métodos: Foi conduzida uma pesquisa bibliográfica nas principais bases de dados electrónicas, Medline, ISI Web of Science e Scopus. Esta foi realizada até 17 de Maio de 2015 e de acordo com o equação de pesquisa. Após a selecção dos artigos, segundo os critérios de inclusão e exclusão, avaliou-se a qualidade metodológica com recurso ao instrumento designado QATSDD (Quality Assessment Tool and Scoring Guidance Notes) e outro instrumento para estudos de intervenção beforeafter, sem grupo de controlo, desenvolvido por profissionais de sáude pertencentes ao National Institutes of Health. A meta-análise foi realizada com a medida do tempo de reacção, usando a diferença das médias padronizadas. O método estatístico utilizado foi Mantel-Haenszel para efeitos aleatórios. Resultados: Conduziu-se a revisão sistemática com quatorze artigos publicados entre 2007 e 2014, três estudos usados na meta-análise. Apenas quatro estudos denotaram melhorias, estatisticamente significativas, no que diz respeito ao efeito de um período de sono no desempenho de uma tarefa motora em crianças. Pelo contrário, a maioria dos estudos é a favor de que um período de sono prejudica o desempenho. Na meta-análise mostra-se que a vigília favorece os valores do tempo de reacção. Conclusões: A presente evidência não suporta o facto de que um período de sono optimiza o desempenho de tarefas de memória procedimental em crianças. Dadas as limitações apresentadas, nomeadamente a heterogeneidade das intervenções e reduzida qualidade metodológica dos estudos incluídos, sem grupo de controlo, sugere-se a realização de estudos futuros com maior nível de evidência.

Scalable Intelligence for Scheduling Systems

A personalização é um aspeto chave de uma interação homem-computador efetiva. Numa era em que existe uma abundância de informação e tantas pessoas a interagir com ela, de muitas maneiras, a capacidade de se ajustar aos seus utilizadores é crucial para qualquer sistema moderno. A criação de sistemas adaptáveis é um domínio bastante complexo que necessita de métodos muito específicos para ter sucesso. No entanto, nos dias de hoje ainda não existe um modelo ou arquitetura padrão para usar nos sistemas adaptativos modernos. A principal motivação desta tese é a proposta de uma arquitetura para modelação do utilizador que seja capaz de incorporar diferentes módulos necessários para criar um sistema com inteligência escalável com técnicas de modelação. Os módulos cooperam de forma a analisar os utilizadores e caracterizar o seu comportamento, usando essa informação para fornecer uma experiência de sistema customizada que irá aumentar não só a usabilidade do sistema mas também a produtividade e conhecimento do utilizador. A arquitetura proposta é constituída por três componentes: uma unidade de informação do utilizador, uma estrutura matemática capaz de classificar os utilizadores e a técnica a usar quando se adapta o conteúdo. A unidade de informação do utilizador é responsável por conhecer os vários tipos de indivíduos que podem usar o sistema, por capturar cada detalhe de interações relevantes entre si e os seus utilizadores e também contém a base de dados que guarda essa informação. A estrutura matemática é o classificador de utilizadores, e tem como tarefa a sua análise e classificação num de três perfis: iniciado, intermédio ou avançado. Tanto as redes de Bayes como as neuronais são utilizadas, e uma explicação de como as preparar e treinar para lidar com a informação do utilizador é apresentada. Com o perfil do utilizador definido torna-se necessária uma técnica para adaptar o conteúdo do sistema. Nesta proposta, uma abordagem de iniciativa mista é apresentada tendo como base a liberdade de tanto o utilizador como o sistema controlarem a comunicação entre si. A arquitetura proposta foi desenvolvida como parte integrante do projeto ADSyS - um sistema de escalonamento dinâmico - utilizado para resolver problemas de escalonamento sujeitos a eventos dinâmicos. Possui uma complexidade elevada mesmo para utilizadores frequentes, daí a necessidade de adaptar o seu conteúdo de forma a aumentar a sua usabilidade. Com o objetivo de avaliar as contribuições deste trabalho, um estudo computacional acerca do reconhecimento dos utilizadores foi desenvolvido, tendo por base duas sessões de avaliação de usabilidade com grupos de utilizadores distintos. Foi possível concluir acerca dos benefícios na utilização de técnicas de modelação do utilizador com a arquitetura proposta.

Heurísticas de pesquisa local para problemas de máquina única

O escalonamento é uma das decisões mais importantes no funcionamento de uma linha de produção. No âmbito desta dissertação foi realizada uma descrição do problema do escalonamento, identificando alguns métodos para a optimização dos problemas de escalonamento. Foi realizado um estudo ao caso do problema de máquina única através do teste de várias instâncias com o objectivo de minimizar o atraso pesado, aplicando uma Meta-Heurística baseada na Pesquisa Local e dois algoritmos baseados no SB. Os resultados obtidos reflectem que os algoritmos baseados no SB apresentaram resultados mais próximos do óptimo, em relação ao algoritmo baseado na PL. Os resultados obtidos permitem sustentar a hipótese de não existirem algoritmos específicos para os problemas de escalonamento. A melhor forma de encontrar uma solução de boa qualidade em tempo útil é experimentar diferentes algoritmos e comparar o desempenho das soluções obtidas.