Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Power consumption is rapidly becoming the dominant limiting factor for further improvements in computer design. Curiously, this applies both at the "high end" of workstations and servers and the "low end" of handheld devices and embedded computers. At the high-end, the challenge lies in dealing with exponentially growing power densities. At the low-end, there is a demand to make mobile devices more powerful and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels, without fully exploring their synergy with other levels. Most software techniques target either operating systems or compilers but do not explore the interaction between the two layers. These techniques also have not fully explored the potential of virtual machines for power management. In contrast, we are developing a system that integrates information from multiple levels of software and hardware, connecting these levels through a communication channel. At the heart of this system are a virtual machine that compiles and dynamically profiles code, and an optimizer that reoptimizes all code, including that of applications and the virtual machine itself. We believe this introspective, holistic approach enables more informed power-management decisions

Energy efficient adaptation engines for android applications

Context The energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application. Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained. Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. Results The proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. Conclusion The adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.Junta de Andalucía MAGIC P12-TIC1814Ministerio de Economía y Competitividad TIN2015-64841-RMinisterio de Ciencia, Innovación y Universidades TIN2017-90644-REDTMinisterio de Ciencia, Innovación y Universidades RTI2018-099213-B-I00Universidad de Málaga LEIA UMA18-FEDERJA-15

Towards a Tool-based Development Methodology for Pervasive Computing Applications

Despite much progress, developing a pervasive computing application remains a challenge because of a lack of conceptual frameworks and supporting tools. This challenge involves coping with heterogeneous devices, overcoming the intricacies of distributed systems technologies, working out an architecture for the application, encoding it in a program, writing specific code to test the application, and finally deploying it. This paper presents a design language and a tool suite covering the development life-cycle of a pervasive computing application. The design language allows to define a taxonomy of area-specific building-blocks, abstracting over their heterogeneity. This language also includes a layer to define the architecture of an application, following an architectural pattern commonly used in the pervasive computing domain. Our underlying methodology assigns roles to the stakeholders, providing separation of concerns. Our tool suite includes a compiler that takes design artifacts written in our language as input and generates a programming framework that supports the subsequent development stages, namely implementation, testing, and deployment. Our methodology has been applied on a wide spectrum of areas. Based on these experiments, we assess our approach through three criteria: expressiveness, usability, and productivity

A selective dynamic compiler for embedded Java virtual machine targeting ARM processors

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2004-2005Ce travail présente une nouvelle technique de compilation dynamique sélective pour les systèmes embarqués avec processeurs ARM. Ce compilateur a été intégré dans la plateforme J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Con- figuration). L’objectif principal de notre travail est d’obtenir une machine virtuelle accélérée, légère et compacte prête pour l’exécution sur les systèmes embarqués. Cela est atteint par l’implémentation d’un compilateur dynamique sélectif pour l’architecture ARM dans la Kilo machine virtuelle de Sun (KVM). Ce compilateur est appelé Armed E-Bunny. Premièrement, on présente la plateforme Java, le Java 2 Micro Edition(J2ME) pour les systèmes embarqués et les composants de la machine virtuelle Java. Ensuite, on discute les différentes techniques d’accélération pour la machine virtuelle Java et on détaille le principe de la compilation dynamique. Enfin, on illustre l’architecture, le design (la conception), l’implémentation et les résultats expérimentaux de notre compilateur dynamique sélective Armed E-Bunny. La version modifiée de KVM a été portée sur un ordinateur de poche (PDA) et a été testée en utilisant un benchmark standard de J2ME. Les résultats expérimentaux de la performance montrent une accélération de 360 % par rapport à la dernière version de la KVM de Sun avec un espace mémoire additionnel qui n’excède pas 119 kilobytes.This work presents a new selective dynamic compilation technique targeting ARM 16/32-bit embedded system processors. This compiler is built inside the J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Configuration) platform. The primary objective of our work is to come up with an efficient, lightweight and low-footprint accelerated Java virtual machine ready to be executed on embedded machines. This is achieved by implementing a selective ARM dynamic compiler called Armed E-Bunny into Sun’s Kilobyte Virtual Machine (KVM). We first present the Java platform, Java 2 Micro Edition (J2ME) for embedded systems and Java virtual machine components. Then, we discuss the different acceleration techniques for Java virtual machine and we detail the principle of dynamic compilation. After that we illustrate the architecture, design, implementation and experimental results of our selective dynamic compiler Armed E-Bunny. The modified KVM is ported on a handheld PDA and is tested using standard J2ME benchmarks. The experimental results on its performance demonstrate that a speedup of 360% over the last version of Sun’s KVM is accomplished with a footprint overhead that does not exceed 119 kilobytes

Exploiting Semantic Technologies in Smart Environments and Grids: Emerging Roles and Case Studies

Semantic technologies are currently spreading across several application domains as a reliable and consistent mean to address challenges related to organization, manipulation, visualization and exchange of data and knowledge. Different roles are actually played by these techniques depending on the application domain, on the timing constraints, on the distributed nature of applications, and so on. This paper provides an overview of the roles played by semantic technologies in the domain of smart grids and smart environments, with a particular focus on changes brought by such technologies in the adopted architectures, programming techniques and tools. Motivations driving the adoption of semantics in these different, but strictly intertwined, fields are introduced using a strong application-driven perspective. Two real-world case studies in smart grids and smart environments are presented to exemplify the roles covered by such technologies and the changes they fostered in software engineering processes. Learned lessons are then distilled and future adoption scenarios discussed

Automatic Collection Selection using Machine Learning

Most recent programming languages include a collection framework as part of their standard library (or runtime). Examples are Java, C#, Python and Ruby. The Java Collection Framework provides a number of collection classes, some of which implement the same abstract data type, which makes them interchangeable. Developers can therefore choose between several functionally equivalent options. Since collections have different performance characteristics, and may be allocated in thousands of programs locations, the choice of collection has an important impact on performance. Unfortunately, programmers often make sub-optimal choices when picking their collections.In this licentiate thesis, we consider the problem of building automated tooling, which would help the developer choose among several collection implementations. We consider an existing tool called Brainy, which targets C++, and adapt it to the Java context. In doing so, we investigate how to synthesize benchmarks and analyze their behavior to create training data for automated classification. We propose one new generative model for collection benchmarks and present the challenges that porting JBrainy to Java entails. Finally, we compare JBrainy's suggestions versus greedy search, on five well known benchmarks. Our investigations show that JBrainy's suggestions were almost as effective than those of greedy search in minimizing the running time of programs. However, we also find that Brainy's benchmark synthesis methods do not apply well to the Java context, since they introduce some significant biases