4 research outputs found
Reviewing the Efficiency of Current Power-Saving Approaches Used Among Different Stages of an Android-Application Lifecycle
info:eu-repo/semantics/publishedVersio
Adding Energy Star Rating Schema to Android Applications on Google Play Store an Example of a Preventive Power Saving Model
info:eu-repo/semantics/publishedVersio
Google Play apps ERM: (energy rating model) multi-criteria evaluation model to generate tentative energy ratings for Google Play store apps
A common issue that is shared among Android smartphones users was and still related to saving their batteries power and to avoid the need of using any recharging resources. The tremendous increase in smartphone usage is clearly accompanied by an increase in the need for more energy. This preoperational relationship between modern technology and energy generates energy-greedy apps, and therefore power-hungry end users. With many apps falling under the same category in an app store, these apps usually share similar functionality. Because developers follow different design and development schools, each app has its own energy-consumption habits. Since apps share similar features, an end-user with limited access to recharging resources would prefer an energy-friendly app rather than a popular energy-greedy app. However, app stores give no indication about the energy behaviour of the apps they offer, which causes users to randomly choose apps without understanding their energy-consumption behaviour. Furthermore, with regard to the research questions about the fact that power saving application consumes a lot of electricity, past studies clearly indicate that there is a lot of battery depletion due to several factors. This problem has become a major concern for smartphone users and manufacturers. The main contribution of our research is to design a tool that can act as an effective decision support factor for end users to have an initial indication of the energy-consumption behaviour of an application before installing it. The core idea of the “before-installation” philosophy is simplified by the contradicting concept of installing the app and then having it monitored and optimized. Since processing requires power, avoiding the consumption of some power in order to conserve a larger amount of power should be our priority. So instead, we propose a preventive strategy that requires no processing on any layer of the smartphone. To address this issue, we propose a star-rating evaluation model (SREM), an approach that generates a tentative energy rating label for each app. To that end, SREM adapts current energy-aware refactoring tools to demonstrate the level of energy consumption of an app and presents it in a star-rating schema similar to the Ecolabels used on electrical home appliances.
The SREM will also inspire developers and app providers to come up with multiple energy-greedy versions of the same app in order to suit the needs of different categories of users and rate their own apps.
We proposed adding SREM to Google Play store in order to generate the energy-efficiency label for each app which will act as a guide for both end users and developers without running any processes on the end-users smartphone. Our research also reviews relevant existing literature specifically those covering various energy-saving techniques and tools proposed by various authors for Android smartphones. A secondary analysis has been done by evaluating the past research papers and surveys that has been done to assess the perception of the users regarding the phone power from their battery. In addition, the research highlights an issue that the notifications regarding the power saving shown on the screen seems to exploit a lot of battery. Therefore, this study has been done to reflect the ways that could help the users to save the phone battery without using any power from the same battery in an efficient manner. The research offers an insight into new ways that could be used to more effectively conserve smartphone energy, proposing a framework that involves end users on the process.Um problema comum entre utilizadores de smartphones Android tem sido a necessidade de economizar a energia das baterias, de modo a evitar a utilização de recursos de recarga. O aumento significativo no uso de smartphones tem sido acompanhado por um aumento, tambĂ©m significativo, na necessidade de mais energia. Esta relação operacional entre tecnologia moderna e energia gera aplicações muito exigentes no seu consumo de energia e, portanto, perfis de utilizadores que requerem nĂveis de energia crescentes. Com muitos das aplicações que se enquadram numa mesma categoria da loja de aplicações (Google Store), essas aplicações geralmente tambĂ©m partilham funcionalidades semelhantes. Como os criadores destas aplicações seguem abordagens diferentes de diversas escolas de design e desenvolvimento, cada aplicação possui as suas prĂłprias caraterĂsticas de consumo de energia. Como as aplicações partilham recursos semelhantes, um utilizador final com acesso limitado a recursos de recarga prefere uma aplicação que consome menos energia do que uma aplicação mais exigente em termos de consumo energĂ©tico, ainda que seja popular. No entanto, as lojas de aplicações nĂŁo fornecem uma indicação sobre o comportamento energĂ©tico das aplicações oferecidas, o que faz com que os utilizadores escolham aleatoriamente as suas aplicações sem entenderem o correspondente comportamento de consumo de energia. Adicionalmente, no que diz respeito Ă questĂŁo de investigação, a solução de uma aplicação de economia de energia consume muita eletricidade, o que a torna limitada; estudos anteriores indicam claramente que há muita perda de bateria devido a vários fatores, nĂŁo constituindo solução para muitos utilizadores e para os fabricantes de smartphones. A principal contribuição de nossa pesquisa Ă© projetar uma ferramenta que possa atuar como um fator de suporte Ă decisĂŁo eficaz para que os utilizadores finais tenham uma indicação inicial do comportamento de consumo de energia de uma aplicação, antes de a instalar. A ideia central da filosofia proposta Ă© a de atuar "antes da instalação", evitando assim a situação em se instala uma aplicação para perceber Ă posteriori o seu impacto no consumo energĂ©tico e depois ter que o monitorizar e otimizar (talvez ainda recorrendo a uma aplicação de monitorização do consumo da bateria, o que agrava ainda mais o consumo energĂ©tico). Assim, como o processamento requer energia, Ă© nossa prioridade evitar o consumo de alguma energia para conservar uma quantidade maior de energia. Portanto, Ă© proposta uma estratĂ©gia preventiva que nĂŁo requer processamento em nenhuma camada do smartphone.
Para resolver este problema, Ă© proposto um modelo de avaliação por classificação baseado em nĂveis e identificado por estrelas (SREM). Esta abordagem gera uma etiqueta de classificação energĂ©tica provisĂłria para cada aplicação. Para isso, o SREM adapta as atuais ferramentas de refatoração com reconhecimento de energia para demonstrar o nĂvel de consumo de energia de uma aplicação, apresentando o resultado num esquema de classificação por estrelas semelhante ao dos rĂłtulos ecolĂłgicos usados em eletrodomĂ©sticos. O SREM tambĂ©m se propõe influenciar quem desenvolve e produz as aplicações, a criarem diferentes versões destas, com diferentes perfis de consumo energĂ©tico, de modo a atender Ă s necessidades de diferentes categorias de utilizadores e assim classificar as suas prĂłprias aplicações. Para avaliar a eficiĂŞncia do modelo como um complemento Ă s aplicações da loja Google Play, que atuam como uma rotulagem para orientação dos utilizadores finais. A investigação tambĂ©m analisa a literatura existente relevante, especificamente a que abrange as várias tĂ©cnicas e ferramentas de economia de energia, propostas para smartphones Android. Uma análise secundária foi ainda realizada, focando nos trabalhos de pesquisa que avaliam a perceção dos utilizadores em relação Ă energia do dispositivo, a partir da bateria. Em complemento, a pesquisa destaca um problema de que as notificações sobre a economia de energia mostradas na tela parecem explorar muita bateria. Este estudo permitiu refletir sobre as formas que podem auxiliar os utilizadores a economizar a bateria do telefone sem usar energia da mesma bateria e, mesmo assim, o poderem fazer de maneira eficiente. A pesquisa oferece uma visĂŁo global das alternativas que podem ser usadas para conservar com mais eficiĂŞncia a energia do smartphone, propondo um modelo que envolve os utilizadores finais no processo.Un problème frĂ©quent rencontrĂ© par les utilisateurs de smartphones Android a Ă©tĂ©, tout en l’étant toujours, d’économiser leur batterie et d’éviter la nĂ©cessitĂ© d’utiliser des ressources de recharge. La croissance considĂ©rable de l’utilisation des smartphones s’accompagne clairement d’une augmentation des besoins en Ă©nergie. Cette relation prĂ©opĂ©rationnelle entre la technologie moderne et l’énergie gĂ©nère des applications gourmandes en Ă©nergie, et donc des utilisateurs finaux qui le sont tout autant. De nombreuses applications relevant de la mĂŞme catĂ©gorie dans une boutique partagent gĂ©nĂ©ralement des fonctionnalitĂ©s similaires. Étant donnĂ© que les dĂ©veloppeurs adoptent diffĂ©rentes approches de conception et de dĂ©veloppement, chaque application a ses propres caractĂ©ristiques de consommation d’énergie. Comme les applications partagent des fonctionnalitĂ©s similaires, un utilisateur final disposant d’un accès limitĂ© aux ressources de recharge prĂ©fĂ©rerait une application Ă©coĂ©nergĂ©tique plutĂ´t qu’une autre gourmande en Ă©nergie. Cependant, les boutiques d’applications ne donnent aucune indication sur le comportement Ă©nergĂ©tique des applications qu’elles proposent, ce qui incite les utilisateurs Ă choisir des applications au hasard sans comprendre leurs caractĂ©ristiques en ce domaine. En outre, en ce qui concerne les questions de recherche sur le fait que les applications d’économie d’énergie consomment beaucoup d’électricitĂ©, des Ă©tudes antĂ©rieures indiquent clairement que la dĂ©charge d’une batterie est due Ă plusieurs facteurs. Ce problème est devenu une prĂ©occupation majeure pour les utilisateurs et les fabricants de smartphones. La principale contribution de notre Ă©tude est de concevoir un outil qui peut agir comme un facteur d’aide efficace Ă la dĂ©cision pour que les utilisateurs finaux aient une indication initiale du comportement de consommation d’énergie d’une application avant de l’installer. L’idĂ©e de base de la philosophie « avant l’installation » est simplifiĂ©e par le concept contradictoire d’installer l’application pour ensuite la contrĂ´ler et l’optimiser. Puisque les opĂ©rations de traitement exigent de l’énergie, Ă©viter la consommation d’une partie d’entre elles pour l’économiser devrait ĂŞtre notre prioritĂ©. Nous proposons donc une stratĂ©gie prĂ©ventive qui ne nĂ©cessite aucun traitement sur une couche quelconque du smartphone. Pour rĂ©soudre ce problème, nous proposons un modèle d’évaluation au moyen d’étoiles (star-rating evaluation model ou SREM), une approche qui gĂ©nère une note Ă©nergĂ©tique indicative pour chaque application. Ă€ cette fin, le SREM adapte les outils actuels de refactoring sensibles Ă l’énergie pour dĂ©montrer le niveau de consommation d’énergie d’une application et la prĂ©sente dans un schĂ©ma de classement par Ă©toiles similaire aux labels Ă©cologiques utilisĂ©s sur les appareils Ă©lectromĂ©nagers. Le SREM incitera Ă©galement les dĂ©veloppeurs et les fournisseurs d’applications Ă mettre au point plusieurs versions avides d’énergie d’une mĂŞme application afin de rĂ©pondre aux besoins des diffĂ©rentes catĂ©gories d’utilisateurs et d’évaluer leurs propres applications. Nous avons proposĂ© d’ajouter le SREM au Google Play Store afin de gĂ©nĂ©rer le label d’efficacitĂ© Ă©nergĂ©tique pour chaque application. Celui-ci servira de guide Ă la fois pour les utilisateurs finaux et les dĂ©veloppeurs sans exĂ©cuter de processus sur le smartphone des utilisateurs finaux. Notre recherche passe Ă©galement en revue la littĂ©rature existante pertinente, en particulier celle qui couvre divers outils et techniques d’économie d’énergie proposĂ©s par divers auteurs pour les smartphones Android. Une analyse secondaire a Ă©tĂ© effectuĂ©e en Ă©valuant les documents de recherche et les enquĂŞtes antĂ©rieurs qui ont Ă©tĂ© rĂ©alisĂ©s pour Ă©valuer la perception des utilisateurs concernant l’alimentation tĂ©lĂ©phonique depuis leur batterie. En outre, l’étude met en Ă©vidence un problème selon lequel les notifications concernant les Ă©conomies d’énergie affichĂ©es Ă l’écran semblent elles-mĂŞmes soumettre les batteries Ă une forte utilisation. Par consĂ©quent, cette Ă©tude a Ă©tĂ© entreprise pour reflĂ©ter les façons qui pourraient aider les utilisateurs Ă Ă©conomiser efficacement la batterie de leur tĂ©lĂ©phone sans pour autant la dĂ©charger. L’étude offre un bon aperçu des nouvelles façons d’économiser plus efficacement l’énergie des smartphones, en proposant un cadre qui implique les utilisateurs finaux dans le processus
High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning
We are living in the era of "Big Data", an era characterized by a voluminous amount of available data. Such amount is mainly due to the continuing advances in the computational capabilities for capturing, storing, transmitting and processing data. However, it is not always the volume of data that matters, but rather the "relevant" information that resides in it.
Exactly 70 years ago, Claude Shannon, the father of information theory, was able to quantify the amount of information in a communication scenario based on a probabilistic model of the data. It turns out that Shannon's theory can be adapted to various probability-based information processing fields, ranging from coding theory to machine learning. The computation of some information theoretic quantities, such as the mutual information, can help in setting fundamental limits and devising more efficient algorithms for many inference problems.
This thesis deals with two different, yet intimately related, inference problems in the fields of coding theory and machine learning. We use Bayesian probabilistic formulations for both problems, and we analyse them in the asymptotic high-dimensional regime. The goal of our analysis is to assess the algorithmic performance on the first hand and to predict the Bayes-optimal performance on the second hand, using an information theoretic approach. To this end, we employ powerful analytical tools from statistical physics.
The first problem is a recent forward-error-correction code called sparse superposition code. We consider the extension of such code to a large class of noisy channels by exploiting the similarity with the compressed sensing paradigm. Moreover, we show the amenability of sparse superposition codes to perform
joint distribution matching and channel coding.
In the second problem, we study symmetric rank-one matrix factorization, a prominent model in machine learning and statistics with many applications ranging from community detection to sparse principal component analysis. We provide an explicit expression for the normalized mutual information and the minimum mean-square error of this model in the asymptotic limit. This allows us to prove the optimality of a certain iterative algorithm on a large set of parameters.
A common feature of the two problems stems from the fact that both of them are represented on dense graphical models. Hence, similar message-passing algorithms and analysis tools can be adopted. Furthermore, spatial coupling, a new technique introduced in the context of low-density parity-check (LDPC) codes, can be applied to both problems. Spatial coupling is used in this thesis as a "construction technique" to boost the algorithmic performance and as a "proof technique" to compute some information theoretic quantities.
Moreover, both of our problems retain close connections with spin glass models studied in statistical mechanics of disordered systems. This allows us to use sophisticated techniques developed in statistical physics. In this thesis, we use the potential function predicted by the replica method in order to prove the threshold saturation phenomenon associated with spatially coupled models. Moreover, one of the main contributions of this thesis is proving that the predictions given by the "heuristic" replica method are exact. Hence, our results could be of great interest for the statistical physics community as well, as they help to set a rigorous mathematical foundation of the replica predictions