Improving Android App Responsiveness through Search-Based Frame Rate Reduction

Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in the application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43%), often applications’ test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps’ responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements

Task scheduling model for fog paradigm

Task scheduling in fog paradigm is highly complex and in the literature, there are still few studies. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a model to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization; requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique.The authors are grateful to the Calouste Gulbenkian Foundation for its funding of this research through the Ph.D. scholarship under the reference No. 234242, 2019 - Postgraduate Scholarships for students from PALOP and Timor-Leste.info:eu-repo/semantics/publishedVersio

Multi-Objective Improvement of Android Applications

Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. Previous work on automated improvement of non-functional properties in mobile apps failed to address the inherent trade-offs between such properties. We propose a practical approach and the first open-source tool, GIDroid (2023), for multi-objective automated improvement of Android apps. In particular, we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.Comment: 32 pages, 8 Figure

Context-aware task scheduling slgorithm for the Fog paradigm: a model proposal

FMEC 2022. 7ª Conferência Internacional sobre "Fog e Mobile Edge Computing", realizada em Paris, França, de 12-15 de dezembro de 2022.Task scheduling in fog paradigm is very complex and, in the literature, according to the author’s knowledge there are still few studies. In the cloud architecture, it is widely studied, and, in much research, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.info:eu-repo/semantics/acceptedVersio

Konzepte und Methoden für die Verwendung eines Caches in mobilen Code-Offloading-Systemen

In dieser Arbeit wird der Frage nachgegangen, ob eine Caching Komponente ein Code Offloading System im mobilen Umfeld sinnvoll ergänzen kann. Dazu wird zunächst die Caching Komponente, in Form eines über das Netzwerk erreichbaren Server, konzipiert und implementiert. Zur Anbindung an das Code Offloading System wird ein Netzwerkprotokoll entworfen und in einem zuvor schon vorhandenen Offloading System implementiert. Zuletzt werden die Komponenten in unterschiedlichen Szenarien evaluiert

Task scheduling in the Fog Computing paradigm: proposal of a context-aware model and evaluation of its performance

Os pedidos de execução de aplicações na arquitetura cloud e no paradigma fog são geralmente heterogéneos e o escalonamento nessas arquiteturas é um problema de otimização com múltiplas restrições. Neste artigo, fizemos um levantamento sobre os trabalhos relacionados com o escalonamento na arquitetura cloud e no paradigma fog, identificamos as suas limitações, explorarmos perspetivas de melhorias e propomos um modelo de escalonamento sensíveis ao contexto para o paradigma fog. A solução proposta utiliza a normalização Min-Max, para resolver a heterogeneidade e normalizar os diferentes parâmetros de contexto. A prioridade dos pedidos é definida através da aplicação da técnica de análise de Regressão Linear Múltipla e o escalonamento é feito utilizando a técnica de Otimização de Programação Não Linear Multiobjetivo. Os resultados obtidos a partir de simulações no kit de ferramentas iFogSim, demonstram que a nossa proposta apresenta um melhor desempenho em comparação com as propostas não sensíveis ao contexto.Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different. context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.Os autores agradecem à Fundação Calouste Gulbenkian pelo financiamento desta investigação através da bolsa de doutoramento sob a referência n.º 234242, 2019-Bolsas de Pós-Graduação para estudantes dos PALOP e de Timor-Leste.info:eu-repo/semantics/publishedVersio

A proposal of context-aware scheduling of mobile applications for the fog computing paradigm

Escalonamento na arquitetura cloud e no paradigma fog continuam a apresentar alguns desafios aliciantes. Na cloud, segundo o conhecimento dos autores, ela é amplamente estudada e em muitas pesquisas é abordada na perspetiva de provedores de serviço. Na fog, é muito complexo e, existem poucos estudos. Procurando trazer contributos inovadores nas áreas de escalonamento de tarefas, neste artigo, propomos uma solução para o problema de escalonamento de aplicações móveis sensíveis ao contexto para o paradigma fog computing onde diferentes parâmetros de contexto são normalizados através da normalização Min-Max, as prioridades são definidas através da aplicação da técnica da Regressão Linear Múltipla (RLM) e o escalonamento é feito recorrendo a técnica de Otimização de Programação Não Linear Multi-objetivo (MONLP).Scheduling in cloud architecture and in the fog paradigm continue to present some exciting challenges. In the cloud, according to the authors' knowledge, it is widely studied and in many researches, it is addressed from the perspective of service providers. In fog, it is very complex and there are few studies. Trying to bring innovative contributions in the areas of task scheduling, in this paper we propose a solution to the problem of context-aware scheduling of mobile applications for the fog computing paradigm, where different context parameters are normalized through Min-Max normalization, priorities are defined by applying the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Nonlinear Programming Optimization (MONLP) technique.info:eu-repo/semantics/publishedVersio

Bestimmung der Ausführungszeit von Java-Anwendungen zur Laufzeit

Das Auslagern von Programmcode stellt eine Möglichkeit dar, die Ausdauer und Leistungsfähigkeit akkubetriebener Mobilgeräte zu verbessern. Um feststellen zu können, ob sich das Auslagern lohnt, ist es unter anderem notwendig, die Ausführungszeit von Programmteilen zu bestimmen. In dieser Arbeit wird hierzu eine Verfahrensweise vorgestellt, die ohne Zugriff auf den Quellcode eines laufenden Java-Programms auskommt. Dabei wird durch statische Analyse von Java- Methoden die Häufigkeit der Ausführung ihrer Abschnitte ermittelt und mit Messergebnissen der einzelnen Anweisungen auf die Ausführungsdauer der gesamten Methode geschlossen. Bei der Messung solcher Anweisungen, mit denen die Java Virtual Machine instruiert wird, treten Probleme auf, zu denen diese Arbeit Lösungsansätze und eine mögliche Implementierungsweise liefert. Es wird weiterhin gezeigt, wie durch dynamische Analyse die so gewonnenen Ergebnisse zur Laufzeit verbessert werden können. Aus dieser Arbeit resultiert eine Entscheidungsgrundlage für die Offloading-Komponente, mit der diese fundiert entscheiden kann, ein Programmteil lokal auszuführen oder zu einem entfernten Server zu übertragen, um die Berechnung dort durchführen zu lassen und so Energie zu sparen

High Performance Embedded Computing

Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand for more computational performance to process large amounts of data from multiple data sources with guaranteed processing times. Actuating outside of the required timing bounds may cause the failure of the system, being vital for systems like planes, cars, business monitoring, e-trading, etc. High-Performance and Time-Predictable Embedded Computing presents recent advances in software architecture and tools to support such complex systems, enabling the design of embedded computing devices which are able to deliver high-performance whilst guaranteeing the application required timing bounds. Technical topics discussed in the book include: Parallel embedded platforms Programming models Mapping and scheduling of parallel computations Timing and schedulability analysis Runtimes and operating systemsThe work reflected in this book was done in the scope of the European project P SOCRATES, funded under the FP7 framework program of the European Commission. High-performance and time-predictable embedded computing is ideal for personnel in computer/communication/embedded industries as well as academic staff and master/research students in computer science, embedded systems, cyber-physical systems and internet-of-things