Measuring energy footprint of software features

Abstract—With the proliferation of Software systems and the rise of paradigms such the Internet of Things, Cyber- Physical Systems and Smart Cities to name a few, the energy consumed by software applications is emerging as a major concern. Hence, it has become vital that software engineers have a better understanding of the energy consumed by the code they write. At software level, work so far has focused on measuring the energy consumption at function and application level. In this paper, we propose a novel approach to measure energy consumption at a feature level, cross-cutting multiple functions, classes and systems. We argue the importance of such measurement and the new insight it provides to non-traditional stakeholders such as service providers. We then demonstrate, using an experiment, how the measurement can be done with a combination of tools, namely our program slicing tool (PORBS) and energy measurement tool (Jolinar)

Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning

Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Funding for open access charge: Universidad de Málaga / CBUA. This work is supported by the European Union’s H2020 re search and innovation programme under grant agreement DAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain

Detecting Feature Influences to Quality Attributes in Large and Partially Measured Spaces using Smart Sampling and Dynamic Learning

Publicación Journal First siendo el original: Munoz, D. J., Pinto, M., & Fuentes, L. (2023). Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning. Knowledge-Based Systems, 270, 110558.Emergent application domains (e.g., Edge Computing/Cloud /B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large \textit{Variability Models} (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems -- the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Trabajo financiado por el programa de I+D H2020 de la UE bajo el acuerdo DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER \emph{IRIS} PID2021-122812OB-I00, y \emph{LEIA} UMA18-FEDERIA-157, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Tunka Advanced Instrument for cosmic rays and Gamma Astronomy

The paper is a script of a lecture given at the ISAPP-Baikal summer school in 2018. The lecture gives an overview of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) facility including historical introduction, description of existing and future setups, and outreach and open data activities.Comment: Lectures given at the ISAPP-Baikal Summer School 2018: Exploring the Universe through multiple messengers, 12-21 July 2018, Bol'shie Koty, Russi

Enhancing the environmental sustainability of IT

Emerging technologies for learning report - Article exploring green I

A multisensing setup for the intelligent tire monitoring

The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes

Trehalose alleviates the phenotype of Machado–Joseph disease mouse models

Machado-Joseph disease (MJD), also known as spinocerebellar ataxia type 3, is the most common of the dominantly inherited ataxias worldwide and is characterized by mutant ataxin-3 aggregation and neuronal degeneration. There is no treatment available to block or delay disease progression. In this work we investigated whether trehalose, a natural occurring disaccharide widely used in food and cosmetic industry, would rescue biochemical, behavioral and neuropathological features of an in vitro and of a severe MJD transgenic mouse model.This work was funded by BioBlast Pharma, the ERDF through the Regional Operational Program Center 2020, Competitiveness Factors Operational Program (COMPETE 2020) and National Funds through FCT (Foundation for Science and Technology) - SFRH/BD/87404/2012, BrainHealth2020 projects (CENTRO-01-0145-FEDER-000008), ViraVector (CENTRO-01-0145FEDER-022095), CortaCAGs (POCI-01-0145-FEDER-016719), SpreadSilenc‑ing POCI-01-0145-FEDER-029716 and POCI-01-0145-FEDER-007440, as well as the SynSpread, ESMI and ModelPolyQ under the EU Joint ProgramNeurodegenerative Disease Research (JPND), the last two co-funded bythe European Union H2020 program, GA No. 643417; by National Ataxia Foundation (USA), the American Portuguese Biomedical Research Fund (APBRF) and the Richard Chin and Lily Lock Machado–Joseph Disease Research Fund.info:eu-repo/semantics/publishedVersio

Sustainable car life cycle design, taking inspiration from natural systems and thermodynamics

This paper exposes the search for a tool and method, which from a systems approach, adopts the rules and logic that govern our physical context (biosphere) in order to provide guidelines that the car industry could use to achieve an ideal state for ecological, economical and social sustainability