Detalhes
Nome
Fernando SilvaCluster
InformáticaCargo
Investigador CoordenadorDesde
01 janeiro 2009
Nacionalidade
PortugalCentro
Centro de Sistemas de Computação AvançadaContactos
+351220402963
fernando.silva@inesctec.pt
2023
Autores
Silva, J; Marques, ERB; Lopes, LMB; Silva, FMA;
Publicação
SOFTWARE-PRACTICE & EXPERIENCE
Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.
2023
Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;
Publicação
CoRR
Abstract
2022
Autores
Ribeiro, P; Silva, F; Ferreira Mendes, JF; Laureano, RD;
Publicação
NetSci-X
Abstract
2022
Autores
Ribeiro, P; Silva, F; Mendes, JF; Laureano, R;
Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2022
Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, F;
Publicação
DATA MINING AND KNOWLEDGE DISCOVERY
Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.
Teses supervisionadas
2021
Autor
André lage Sobral
Instituição
UP-FCUP
2021
Autor
Lucas Carvalho de Paula
Instituição
UP-FCUP
2021
Autor
Rafael António Belokurows
Instituição
UP-FCUP
2021
Autor
Ziad Ali Kassam
Instituição
UM
2021
Autor
Joaquim Magalhães Esteves da Silva
Instituição
UP-FCUP
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