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Sobre

Sobre

Concluí o mestrado em Engenharia Informática em 2014, com uma tese intitulad “Monitoring Energy Consumption in Android Applications”, com uma bolsa no projecto GreenSSCM – Green Software for Space Control Missions, na Universidade do Minho.

Atualmente, eu sou um estudante de doutoramento no programa de doutoral MAP-i. Continuo na área de computação com foco em energia / computação verde, uma vez que meu tema de tese de doutoramento tem como título "Energy-aware Software Product Lines".

Sou membro e fundador do Green Software Lab (GSL) do HASLab/INESC TEC na Universidade do Minho, onde agora trabalho como investigador.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Marco Linhares Couto
  • Cluster

    Informática
  • Cargo

    Assistente de Investigação
  • Desde

    01 maio 2014
Publicações

2019

Towards using memoization for saving energy in android

Autores
Rua, R; Couto, M; Pinto, A; Cunha, J; Saraiva, J;

Publicação
XXII Ibero-American Conference on Software Engineering, CIbSE 2019

Abstract
Over the last few years, the interest in the analysis of the energy consumption of Android applications has been increasing significantly. Indeed, there are a considerable number of studies which aim at analyzing the energy consumption in the Android ecosystem, such as measuring/estimating the energy consumed by an application or block of code, or even detecting energy expensive coding patterns or APIs. In this paper, we present an initial study of the impact of memoization in the energy consumption of Android applications. We compare implementations of 18 methods from different applications, with and without using memoization, and measure the energy consumption of both of them. The results show that using memoization can be a good approach for saving energy, since 13 of those methods decreased their energy consumption.

2019

GreenSource: A large-scale collection of android code, tests and energy metrics

Autores
Rua, R; Couto, M; Saraiva, J;

Publicação
IEEE International Working Conference on Mining Software Repositories

Abstract
This paper presents the GreenSource infrastructure: a large body of open source code, executable Android applications, and curated dataset containing energy code metrics. The dataset contains energy metrics obtained by both static analysing the applications' source code and by executing them with available test inputs. To automate the execution of the applications we developed the AnaDroid tool which instruments its code, compiles and executes it with test inputs in any Android device, while collecting energy metrics. GreenSource includes all Android applications included in the MUSE Java source code repository, while AnaDroid implements all Android's energy greedy features described in the literature, GreenSource aims at characterizing energy consumption in the Android ecosystem, providing both Android developers and researchers a setting to reason about energy efficient Android software development. © 2019 IEEE.

2018

Energyware analysis

Autores
Pereira, R; Couto, M; Ribeiro, F; Rua, R; Saraiva, J;

Publicação
CEUR Workshop Proceedings

Abstract
This documents introduces \Energyware" as a software engineering discipline aiming at defining, analyzing and optimizing the energy consumption by software systems. In this paper we present energyware analysis in the context of programming languages, software data structures and program's source code. For each of these areas we describe the research work done in the context of the Green Software Laboratory at Minho University: we describe energyaware techniques, tools, libraries, and repositories. © 2018 by the paper's authors.

2017

Products go Green: Worst-Case Energy Consumption in Software Product Lines

Autores
Couto, M; Borba, P; Cunha, J; Fernandes, JP; Pereira, R; Saraiva, J;

Publicação
Proceedings of the 21st International Systems and Software Product Line Conference, SPLC 2017, Volume A, Sevilla, Spain, September 25-29, 2017

Abstract
The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly. In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them. Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product. We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products. © 2017 ACM.

2017

Energy efficiency across programming languages: how do energy, time, and memory relate?

Autores
Pereira, R; Couto, M; Ribeiro, F; Rua, R; Cunha, J; Fernandes, JP; Saraiva, J;

Publicação
Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering, SLE 2017, Vancouver, BC, Canada, October 23-24, 2017

Abstract