Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

In-silico research has grown considerably. Today's scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.Fil: Longo, Mathias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. University of Southern California; Estados UnidosFil: Rodriguez, Ana Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Using bad smell-driven code refactorings in mobile applications to reduce battery usage

Mobile devices are the most popular kind of computational device in the world. These devices have more limited resources than personal computers, and more importantly, battery consumption is always an issue since mobile devices rely on their battery as energy supply. On the other hand, to date, many applications are developed using the object-oriented (OO) paradigm, which has some inherent features, such as object creation, that inherently consume energy in the context of mobile development. These features at the same time enable for maintainability, flexibility, among other software quality-related advantages. Moreover, known code refactorings driven by bad smells can be applied to mobile applications to produce good OO designs, at the expense of potentially consuming more energy. Then, this paper presents an analysis to evaluate the preliminary trade-off between OO design purity and battery consumption.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Using bad smell-driven code refactorings in mobile applications to reduce battery usage

Mobile devices are the most popular kind of computational device in the world. These devices have more limited resources than personal computers, and more importantly, battery consumption is always an issue since mobile devices rely on their battery as energy supply. On the other hand, to date, many applications are developed using the object-oriented (OO) paradigm, which has some inherent features, such as object creation, that inherently consume energy in the context of mobile development. These features at the same time enable for maintainability, flexibility, among other software quality-related advantages. Moreover, known code refactorings driven by bad smells can be applied to mobile applications to produce good OO designs, at the expense of potentially consuming more energy. Then, this paper presents an analysis to evaluate the preliminary trade-off between OO design purity and battery consumption.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Tales from the Code #1: The Effective Impact of Code Refactorings on Software Energy Consumption

International audienceSoftware maintenance and evolution enclose a broad set of actions that aim to improve both functional and non-functional concerns of a software system. Among the non-functional concerns, energy consumption is getting more and more traction in the industry, no matter the software is mobile or deployed in the cloud. In this context, the impact of code refactorings on energy consumption remains unclear, though. In particular, while the state of the art investigated the impact of some specific code refactorings on dedicated benchmarks, we miss an assessment that those apply to more comprehensive and complex software. To address this threat, this paper studies the evolution of the energy consumption of 7 open-source software developed for more than 5 years. Then, by focusing on the impact on energy consumption of changes involving code refactorings, we intend to assess the effects induced by such code refactorings in practice. For all these software systems we studied, our empirical results report that the code refactorings we mined do not substantially impact energy consumption. Interestingly, these results highlight that i) structural code refactorings bring energy-preserving changes to the code, and ii) major energy variations seem to be related to functional and computational code evolutions

Comparing the Energy Consumption of Java I/O Libraries and Methods

International audienceThe Java language is rich of native and third-party I/O APIs that most Java applications and software use. Such operations can even be considered core to most software as they allow the interaction with the user and its data in a nonevolatile way. Yet, the I/Os captivate a lot of attention due to their importance, but also due to the cost that these relatively slow operations add to read and write precious data, most commonly from/to disks. In this context, the impact of these I/O operations on the energy consumption didn't get as much attention. Of course, I/O operations are responsible for energy consumption at the level of the storage medium (HDD or SDD) but they also induce non negligible costs-both performance and energy-wiseat the CPU level. However, only few works take into account the impact of I/O on the energy consumption, especially at the CPU-level. Hence, this paper elaborates a detailed study with two main objectives. First we aim at assessing the energy consumption of several well-known I/O libraries methods, and investigate if different read/write methods can exhibit different energy consumption. Concretely, we assess-using micro-benchmarksthe energy consumption of 27 I/O methods for several file sizes and establish the truth about the most and least energy efficient methods. The second objective is to validate the results of the first experiments on real Java projects by substituting their default I/O methods and measuring the before/after energy consumption. Our results showed that i) different I/O methods consume very different amounts of energy, such as NIO Channels that are 20% more efficient than other methods for read purposes ii) substituting the I/O method in a software by a more efficient one can save an important amount of energy, 15% of energy saving has been registered for K-nucleotide and 3% for Zip4j. we also showed that choosing the right I/O method can save more than 30% of energy consumption when using the Javax.crypto API. Our work offers direct conclusions and guidelines on which I/O methods to use in which situation (read all data, read specific data, write data, etc.) for a better energy efficiency. It also open doors for other works to better optimize the energy consumption of the I/O APIs and methods

Minimizing evolutionary algorithms energy consumption in the low-level language Zig

Managing energy resources in scientific computing implies awareness of a wide range of software engineering techniques that, when applied, can minimize the energy footprint of experiments. In the case of evolutionary computation, we are talking about a specific workload that includes the generation of chromosomes and operations that change parts of them or access and operate on them to obtain a fitness value. In a low-level language such as Zig, we will show how different choices will affect the energy consumption of an experiment.PID2020-115570GB-C22 (DemocratAI::UGR

On the performance of WebAssembly

Dissertação de mestrado integrado em Informatics EngineeringThe worldwide Web has dramatically evolved in recent years. Web pages are dynamic, expressed by pro grams written in common programming languages given rise to sophisticated Web applications. Thus, Web browsers are almost operating systems, having to interpret/compile such programs and execute them. Although JavaScript is widely used to express dynamic Web pages, it has several shortcomings and performance inefficiencies. To overcome such limitations, major IT powerhouses are developing a new portable and size/load efficient language: WebAssembly. In this dissertation, we conduct the first systematic study on the energy and run-time performance of WebAssembly and JavaScript on the Web. We used micro-benchmarks and real applications to have more realistic results. The results show that WebAssembly, while still in its infancy, is starting to already outperform JavaScript, with much more room to grow. A statistical analysis indicates that WebAssembly produces significant performance differences compared to JavaScript. However, these differences differ between micro-benchmarks and real-world benchmarks. Our results also show that WebAssembly improved energy efficiency by 30%, on average, and show how different WebAssembly behaviour is among three popular Web Browsers: Google Chrome, Microsoft Edge, and Mozilla Firefox. Our findings indicate that WebAssembly is faster than JavaScript and even more energy-efficient. Our benchmarking framework is also available to allow further research and replication.A Web evoluiu dramaticamente em todo o mundo nos últimos anos. As páginas Web são dinâmicas, expressas por programas escritos em linguagens de programação comuns, dando origem a aplicativos Web sofisticados. Assim, os navegadores Web são quase como sistemas operacionais, tendo que interpre tar/compilar tais programas e executá-los. Embora o JavaScript seja amplamente usado para expressar páginas Web dinâmicas, ele tem várias deficiências e ineficiências de desempenho. Para superar tais limitações, as principais potências de TI estão a desenvolver uma nova linguagem portátil e eficiente em tamanho/carregamento: WebAssembly. Nesta dissertação, conduzimos o primeiro estudo sistemático sobre o desempenho da energia e do tempo de execução do WebAssembly e JavaScript na Web. Usamos micro-benchmarks e aplicações reais para obter resultados mais realistas. Os resultados mostram que WebAssembly, embora ainda esteja na sua infância, já está começa a superar o JavaScript, com muito mais espaço para crescer. Uma análise estatística indica que WebAssembly produz diferenças de desempenho significativas em relação ao JavaScript. No entanto, essas diferenças diferem entre micro-benchmarks e benchmarks de aplicações reais. Os nossos resultados também mostram que o WebAssembly melhorou a eficiência energética em 30%, em média, e mostram como o comportamento do WebAssembly é diferente entre três navegadores Web populares: Google Chrome, Microsoft Edge e Mozilla Firefox. As nossas descobertas indicam que o WebAssembly é mais rápido que o JavaScript e ainda mais eficiente em termos de energia. A nossa benchmarking framework está disponível para permitir pesquisas adicionais e replicação

Energyware engineering: techniques and tools for green software development

Tese de Doutoramento em Informática (MAP-i)Energy consumption is nowadays one of the most important concerns worldwide. While hardware is generally seen as the main culprit for a computer’s energy usage, software too has a tremendous impact on the energy spent, as it can cancel the efficiency introduced by the hardware. Green Computing is not a newfield of study, but the focus has been, until recently, on hardware. While there has been advancements in Green Software techniques, there is still not enough support for software developers so they can make their code more energy-aware, with various studies arguing there is both a lack of knowledge and lack of tools for energy-aware development. This thesis intends to tackle these two problems and aims at further pushing forward research on Green Software. This software energy consumption issue is faced as a software engineering question. By using systematic, disciplined, and quantifiable approaches to the development, operation, and maintenance of software we defined several techniques, methodologies, and tools within this document. These focus on providing software developers more knowledge and tools to help with energy-aware software development, or Energyware Engineering. Insights are provided on the energy influence of several stages performed during a software’s development process. We look at the energy efficiency of various popular programming languages, understanding which are the most appropriate if a developer’s concern is energy consumption. A detailed study on the energy profiles of different Java data structures is also presented, alongwith a technique and tool, further providing more knowledge on what energy efficient alternatives a developer has to choose from. To help developers with the lack of tools, we defined and implemented a technique to detect energy inefficient fragments within the source code of a software system. This technique and tool has been shown to help developers improve the energy efficiency of their programs, and even outperforming a runtime profiler. Finally, answers are provided to common questions and misconceptions within this field of research, such as the relationship between time and energy, and howone can improve their software’s energy consumption. This thesis provides a great effort to help support both research and education on this topic, helps continue to grow green software out of its infancy, and contributes to solving the lack of knowledge and tools which exist for Energyware Engineering.Hoje em dia o consumo energético é uma das maiores preocupações a nível global. Apesar do hardware ser, de umaforma geral, o principal culpado para o consumo de energia num computador, o software tem também um impacto significativo na energia consumida, pois pode anular, em parte, a eficiência introduzida pelo hardware. Embora Green Computing não seja uma área de investigação nova, o foco tem sido, até recentemente, na componente de hardware. Embora as técnicas de Green Software tenham vindo a evoluir, não há ainda suporte suficiente para que os programadores possam produzir código com consciencialização energética. De facto existemvários estudos que defendem que existe tanto uma falta de conhecimento como uma escassez de ferramentas para o desenvolvimento energeticamente consciente. Esta tese pretende abordar estes dois problemas e tem como foco promover avanços em green software. O tópico do consumo de energia é abordado duma perspectiva de engenharia de software. Através do uso de abordagens sistemáticas, disciplinadas e quantificáveis no processo de desenvolvimento, operação e manutencão de software, foi possível a definição de novas metodologias e ferramentas, apresentadas neste documento. Estas ferramentas e metodologias têm como foco dotar de conhecimento e ferramentas os programadores de software, de modo a suportar um desenvolvimento energeticamente consciente, ou Energyware Engineering. Deste trabalho resulta a compreensão sobre a influência energética a ser usada durante as diferentes fases do processo de desenvolvimento de software. Observamos as linguagens de programação mais populares sobre um ponto de vista de eficiência energética, percebendo quais as mais apropriadas caso o programador tenha uma preocupação com o consumo energético. Apresentamos também um estudo detalhado sobre perfis energéticos de diferentes estruturas de dados em Java, acompanhado por técnicas e ferramentas, fornecendo conhecimento relativo a quais as alternativas energeticamente eficientes que os programadores dispõem. Por forma a ajudar os programadores, definimos e implementamos uma técnica para detetar fragmentos energicamente ineficientes dentro do código fonte de um sistema de software. Esta técnica e ferramenta têm demonstrado ajudar programadores a melhorarem a eficiência energética dos seus programas e em algum casos superando um runtime profiler. Por fim, são dadas respostas a questões e conceções erradamente formuladas dentro desta área de investigação, tais como o relacionamento entre tempo e energia e como é possível melhorar o consumo de energia do software. Foi empregue nesta tese um esforço árduo de suporte tanto na investigação como na educação relativo a este tópico, ajudando à maturação e crescimento de green computing, contribuindo para a resolução da lacuna de conhecimento e ferramentas para suporte a Energyware Engineering.This work is partially funded by FCT – Foundation for Science and Technology, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operacional Programme for Human Capital (POCH), with scholarship reference SFRH/BD/112733/2015. Additionally, funding was also provided the ERDF – European Regional Development Fund – through the Operational Programmes for Competitiveness and Internationalisation COMPETE and COMPETE 2020, and by the Portuguese Government through FCT project Green Software Lab (ref. POCI-01-0145-FEDER-016718), by the project GreenSSCM - Green Software for Space Missions Control, a project financed by the Innovation Agency, SA, Northern Regional Operational Programme, Financial Incentive Grant Agreement under the Incentive Research and Development System, Project No. 38973, and by the Luso-American Foundation in collaboration with the National Science Foundation with grant FLAD/NSF ref. 300/2015 and ref. 275/2016

Explainable, Security-Aware and Dependency-Aware Framework for Intelligent Software Refactoring

As software systems continue to grow in size and complexity, their maintenance continues to become more challenging and costly. Even for the most technologically sophisticated and competent organizations, building and maintaining high-performing software applications with high-quality-code is an extremely challenging and expensive endeavor. Software Refactoring is widely recognized as the key component for maintaining high-quality software by restructuring existing code and reducing technical debt. However, refactoring is difficult to achieve and often neglected due to several limitations in the existing refactoring techniques that reduce their effectiveness. These limitation include, but not limited to, detecting refactoring opportunities, recommending specific refactoring activities, and explaining the recommended changes. Existing techniques are mainly focused on the use of quality metrics such as coupling, cohesion, and the Quality Metrics for Object Oriented Design (QMOOD). However, there are many other factors identified in this work to assist and facilitate different maintenance activities for developers: 1. To structure the refactoring field and existing research results, this dissertation provides the most scalable and comprehensive systematic literature review analyzing the results of 3183 research papers on refactoring covering the last three decades. Based on this survey, we created a taxonomy to classify the existing research, identified research trends and highlighted gaps in the literature for further research. 2. To draw attention to what should be the current refactoring research focus from the developers’ perspective, we carried out the first large scale refactoring study on the most popular online Q&A forum for developers, Stack Overflow. We collected and analyzed posts to identify what developers ask about refactoring, the challenges that practitioners face when refactoring software systems, and what should be the current refactoring research focus from the developers’ perspective. 3. To improve the detection of refactoring opportunities in terms of quality and security in the context of mobile apps, we designed a framework that recommends the files to be refactored based on user reviews. We also considered the detection of refactoring opportunities in the context of web services. We proposed a machine learning-based approach that helps service providers and subscribers predict the quality of service with the least costs. Furthermore, to help developers make an accurate assessment of the quality of their software systems and decide if the code should be refactored, we propose a clustering-based approach to automatically identify the preferred benchmark to use for the quality assessment of a project. 4. Regarding the refactoring generation process, we proposed different techniques to enhance the change operators and seeding mechanism by using the history of applied refactorings and incorporating refactoring dependencies in order to improve the quality of the refactoring solutions. We also introduced the security aspect when generating refactoring recommendations, by investigating the possible impact of improving different quality attributes on a set of security metrics and finding the best trade-off between them. In another approach, we recommend refactorings to prioritize fixing quality issues in security-critical files, improve quality attributes and remove code smells. All the above contributions were validated at the large scale on thousands of open source and industry projects in collaboration with industry partners and the open source community. The contributions of this dissertation are integrated in a cloud-based refactoring framework which is currently used by practitioners.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/171082/1/Chaima Abid Final Dissertation.pdfDescription of Chaima Abid Final Dissertation.pdf : Dissertatio