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About

About

I completed my MsC degree in Informatics Engineering in 2014, with a thesis entitled “Monitoring Energy Consumption in Android Applications”, with a scholarship in a project called GreenSSCM – Green Software for Space Control Missions, at the University of Minho.

Currently, I am a PhD student in the MAP-i doctoral program. I continue to on the energy-aware software/green computing area, since my PhD thesis theme is “Energy-aware Software Product Lines”.

I am a member and one of the founders of the Green Software Lab (GSL) at the HASLab/INESC TEC at the University of Minho, where I am working now as a researcher.

 

Interest
Topics
Details

Details

  • Name

    Marco Linhares Couto
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    01st May 2014
Publications

2018

Energyware analysis

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

Publication
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

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

Publication
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?

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

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

Abstract

2017

Towards a Green Ranking for Programming Languages

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

Publication
PROCEEDINGS OF THE 21ST BRAZILIAN SYMPOSIUM ON PROGRAMMING LANGUAGES (SBLP 2017)

Abstract
While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so. In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each language. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.

2017

Helping programmers improve the energy efficiency of source code

Authors
Pereira, R; Carcao, T; Couto, M; Cunha, J; Fernandes, JP; Saraiva, J;

Publication
Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017 - Companion Volume

Abstract