Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Green IT - available data and guidelines for reducing energy consumption in IT Systems

Nowadays saving energy is an interdisciplinary key challenge. Green IT deals with saving energy in IT systems, and is rapidly gaining momentum. Hardware manufacturers and designers have first considered the problem, in the field of IT, but recently software energy efficiency gathered the interest of industry and academic research. In this paper we aim at summarizing the available knowledge in Green IT. In particular we: • Introduce a taxonomy of concepts related to energy and IT. • Present recent data on energy consumption trends organized according to the taxonomy. • Present some guidelines to write energy efficient software organized according to the taxonomy. • Underline what is missing and what should be done in future research

Creativity embedding: A vector to characterise and classify plausible triples in deep learning NLP models

In this paper we define the creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks. We use as basic unit the notion of triple (head, relation, tail). We investigate if additional information about creativity improves natural language processing tasks. In this work, we focus on triple plausibility task, exploiting BERT model and a WordNet11 dataset sample. Contrary to our hypothesis, we do not detect increase in the performance

Definition, implementation and validation of energy code smells: an exploratory study on an embedded system

Optimizing software in terms of energy efficiency is one of the challenges that both research and industry will have to face in the next few years.We consider energy efficiency as a software product quality characteristic, to be improved through the refactoring of appropriate code pattern: the aim of this work is identifying those code patterns, hereby defined as Energy Code Smells, that might increase the impact of software over power consumption. For our purposes, we perform an experiment consisting in the execution of several code patterns on an embedded system. These code patterns are executed in two versions: the first one contains a code issue that could negatively impact power consumption, the other one is refactored removing the issue. We measure the power consumption of the embedded device during the execution of each code pattern. We also track the execution time to investigate whether Energy Code Smells are also Performance Smells. Our results show that some Energy Code Smells actually have an impact over power consumption in the magnitude order of micro Watts. Moreover, those Smells did not introduce a performance decrease

Analyses of a novel SCN5A mutation (C1850S): conduction vs. repolarization disorder hypotheses in the Brugada syndrome.

AIMS: Brugada syndrome (BrS) is characterized by arrhythmias leading to sudden cardiac death. BrS is caused, in part, by mutations in the SCN5A gene, which encodes the sodium channel alpha-subunit Na(v)1.5. Here, we aimed to characterize the biophysical properties and consequences of a novel BrS SCN5A mutation. METHODS AND RESULTS: SCN5A was screened for mutations in a male patient with type-1 BrS pattern ECG. Wild-type (WT) and mutant Na(v)1.5 channels were expressed in HEK293 cells. Sodium currents (I(Na)) were analysed using the whole-cell patch-clamp technique at 37 degrees C. The electrophysiological effects of the mutation were simulated using the Luo-Rudy model, into which the transient outward current (I(to)) was incorporated. A new mutation (C1850S) was identified in the Na(v)1.5 C-terminal domain. In HEK293 cells, mutant I(Na) density was decreased by 62% at -20 mV. Inactivation of mutant I(Na) was accelerated in a voltage-dependent manner and the steady-state inactivation curve was shifted by 11.6 mV towards negative potentials. No change was observed regarding activation characteristics. Altogether, these biophysical alterations decreased the availability of I(Na). In the simulations, the I(to) density necessary to precipitate repolarization differed minimally between the two genotypes. In contrast, the mutation greatly affected conduction across a structural heterogeneity and precipitated conduction block. CONCLUSION: Our data confirm that mutations of the C-terminal domain of Na(v)1.5 alter the inactivation of the channel and support the notion that conduction alterations may play a significant role in the pathogenesis of BrS