Artigos

URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11798

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Exploring potential implementations of PCE in IoT world
(Optical Switching and Networking, 2017-11) Souza, Vitor Barbosa C.; Ramirez, Wilson; Marin-Tordera, Eva; Sanchez, Sergio
The recently coined Internet of Things (IoT) paradigm leverages a large volume of heterogeneous Network Elements (NEs) demanding broad connectivity anywhere, anytime and anyhow, fueling the deployment of innovative Internet services, such as Cloud or Fog Computing, Data Center Networks (DCNs), Smart Cities or Smart Transportation. The proper deployment of these novel Internet services is imposing hard connectivity constraints, such as high transmission capacity, reliable communications, as well as an efficient control scheme capable of enabling an agile coordination of actions in large heterogeneous scenarios. In recent years, novel control schemes, such as the so-called Path Computation Element (PCE) has gained momentum in the network research community turning into real PCE implementations. Indeed, there is a wealth of studies assessing the PCE performance, clearly showing the potential benefits of decoupling routing control tasks from the forwarding nodes. Nevertheless, recognized the need for a control solution in IoT scenarios, there is not much published information analyzing PCE benefits in these IoT scenarios. In this paper, we distill how the PCE may gracefully provide for service composition in an agile manner, handling the specific constraints and requirements found in IoT scenarios. To this end, we propose a novel PCE strategy referred to as Service-Oriented PCE (SPCE), which enables network-aware service composition.
Fast placement and routing by extending coarse-grained reconfigurable arrays with Omega Networks
(Journal of Systems Architecture, 2011-09) Ferreira, Ricardo S.; Cardoso, João M. P.; Damiany, Alex; Vendramini, Julio; Teixeira, Tiago
Reconfigurable computing architectures are commonly used for accelerating applications and/or for achieving energy savings. However, most reconfigurable computing architectures suffer from computationally demanding placement and routing (P&R) steps. This problem may disable their use in systems requiring dynamic compilation (e.g., to guarantee application portability in embedded systems). Bearing in mind the simplification of P&R steps, this paper presents and analyzes a coarse-grained reconfigurable array (CGRA) extended with global multistage interconnect networks, specifically Omega Networks. We show that integrating one or two Omega Networks in a CGRA permits to simplify the P&R stage resulting in both low hardware resource overhead and low performance degradation (18% for an 8 × 8 array). We compare the proposed CGRA, which integrates one or two Omega Networks, with a CGRA based on a grid of processing elements with reach neighbor interconnections and with a torus topology. The execution time needed to perform the P&R stage for the two array architectures shows that the array using two Omega Networks needs a far simpler and faster P&R. The P&R stage in our approach completed on average in about 16× less time for the 17 benchmarks used. Similar fast approaches needed CGRAs with more complex interconnect resources in order to allow most of the benchmarks used to be successfully placed and routed.
Iterated greedy with random variable neighborhood descent for scheduling jobs on parallel machines with deterioration effect
(Electronic Notes in Discrete Mathematics, 2017-04-14) Santos, Vívian L. Aguiar; Arroyo, José Elias C.
In this paper, we study an unrelated parallel machine scheduling problem in which the jobs cause deterioration of the machines. This deterioration decreases the performance of the machines, and therefore, the processing times of the jobs are increased over time. The problem is to find the processing sequence of jobs on each machine in order to reduce the deterioration of the machines and consequently minimize the makespan. This problem is NP-hard when the number of machines is greater or equal than two, and hence we propose a heuristic based on the Iterated Greedy meta-heuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in local search phase. The performance of our heuristic, named IG-RVND, is compared with the state-of-the-art meta-heuristic proposed in the literature for the problem under study. The results show that the our heuristic outperform the existing algorithm by a significant margin.
NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making
(Artificial Intelligence in Medicine, 2014-10-05) Cerqueira, Fabio Ribeiro; Ferreira, Tiago Geraldo; Oliveira, Alcione de Paiva; Augusto, Douglas Adriano; Krempser, Eduardo; Barbosa, Helio José Corrêa; Franceschini, Sylvia do Carmo Castro; Freitas, Brunnella Alcantara Chagas de; Gomes, Andreia Patricia; Siqueira-Batista, Rodrigo
This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units. However, unlike previous scoring systems, our computational tool is not intended to be used at the patients bedside, although it is possible. Our primary goal is to deliver a computational system to aid medical research in understanding the correlation of key variables with the studied outcome so that new standards can be established for future clinical decisions. In the implemented simulation environment, the values of key attributes can be changed using a user-friendly interface, where the impact of each change on the outcome is immediately reported, allowing a quantitative analysis, in addition to a qualitative investigation, and delivering a totally interactive computational tool that facilitates hypothesis construction and testing. Our statistical experiments showed that the resulting model for death prediction could achieve an accuracy of 86.7% and an area under the receiver operating characteristic curve of 0.84 for the positive class. Using this model, three physicians and a neonatal nutritionist performed simulations with key variables correlated with chance of death. The results indicated important tendencies for the effect of each variable and the combination of variables on prognosis. We could also observe values of gestational age and birth weight for which a low Apgar score and the occurrence of respiratory distress syndrome (RDS) could be less or more severe. For instance, we have noticed that for a newborn with 2000 g or more the occurrence of RDS is far less problematic than for neonates weighing less. The significant accuracy demonstrated by our predictive model shows that NICeSim might be used for hypothesis testing to minimize in vivo experiments. We observed that the model delivers predictions that are in very good agreement with the literature, demonstrating that NICeSim might be an important tool for supporting decision making in medical practice. Other very important characteristics of NICeSim are its flexibility and dynamism. NICeSim is flexible because it allows the inclusion and deletion of variables according to the requirements of a particular study. It is also dynamic because it trains a just-in-time model. Therefore, the system is improved as data from new patients become available. Finally, NICeSim can be extended in a cooperative manner because it is an open-source system.