Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This work presents an approach for the architecture analysis of energy efficiency for static and self-adaptive software systems. It introduces a modeling language that captures consumption characteristics on an architectural level. The outlined analysis predicts the energy efficiency of systems described with this language. Lastly, this work introduces an approach for considering transient effects in design time architecture analyses

Automated Reliability Prediction and Analysis from Software Architectures

Tese de doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologias da Universidade de CoimbraThe quality of a software is determined by how it meets non-functional requirements such as performance, reliability, availability, maintainability and other ‘-ilities’. Depending on the application context, certain qualities are more critical to attain than others. As an example, a web-server processing large amounts of data should present qualities regarding to performance, while a software applied to a medical context must assure that no human life is at risk and as such, should comply to safety as a quality requirement. In a software engineering perspective, quality requirements should be assessed throughout the software development life-cycle. In an early stage, quality assessment supports design decisions and promotes analysis of possible alternatives. During the implementation or testing stages, project managers may confirm that the design meets the developed product and assure that it will conform to the stakeholder’s requirements. Regarding evolutionary stages, architects can also compare different designs and decide for the most suitable solution ta- king into account the desired quality attributes. During the development of a software system, neglecting the assessment of the quality requirements may lead, sooner or later, to the developed product failing to achieve one or more non-functional attributes desired by the stakeholder. Consequently, the development process returns to a previous phase for re-designing, re-implement and re-test a new solution to solve the problem. In short, it will involve more time, effort and money, causing more costs to the whole software project. Software architecture plays an important role in the achievement of non-functional attributes. Designers use architectures to codify non- functional properties and employ good design practices. In addition, it allows to maintain the traceability of the project through its lifespan and also serve as a form of communication between stakeholders, developers and maintainers. Software architectures can be considered one of the first documents in the project to structure the system, since allows to describe the development plans and specify rules, properties and architectural styles to attain specific quality attributes. For these reasons, the existent techniques to assess quality attributes use software architectures to obtain information about the system and provide accurate quantitative results. The problem addressed by this thesis resides in the fact that in today’s world most of the methods to assess quality attributes from a software architecture are still manually performed. To quantitatively assess an architecture’s quality attribute, designers have to build mathematical models through manual tasks and rebuild them for every change per- formed in the architecture. As any other manual task, building such models is error-prone, time consuming and can be almost unfeasible for large and complex systems. With this in mind, this thesis proposes to fill a gap in research by investigating towards a method that automatically assesses the reli- ability as a quality attribute from a software architecture. In particular, we exploit the formalisms of Architecture Description Languages (ADLs) to automatically generate mathematical models ex- pressing the reliability behavior of a system. Then, we extended the notion of ‘automated assessment’ to perform a thorough analysis to identify architectural weak points that are affecting the system. This analysis aims to provide information for architects about reliability improvements and suggest alternatives. With the goal of providing an assisting tool to aid architects in the design process, we implemented a plugin integrated in a ADL design tool. This plugin aims to make our automated approach available for architects to test and analyze their designs regarding reliability. In addition, we showed the different application contexts of our approach by including it in the reasoning process of self-adaptive systems. The results showed an improvement in the overall system quality when comparing to the traditional planning approaches. To conclude, we validated our method through a set of experiments that put into comparison our method with others that used manual approaches to assess reliability. In this work we pursue the motivation of contributing with a set of methods to give support for practitioners and researchers to avoid, prevent and detect undesired or unfeasible architectural designs. Moreover, we intend to promote the development of software with better quality and assure that it meets the desired quality requirements during the development process.FCT - SFRH/BD/89702/201

Continuous Rationale Management

Continuous Software Engineering (CSE) is a software life cycle model open to frequent changes in requirements or technology. During CSE, software developers continuously make decisions on the requirements and design of the software or the development process. They establish essential decision knowledge, which they need to document and share so that it supports the evolution and changes of the software. The management of decision knowledge is called rationale management. Rationale management provides an opportunity to support the change process during CSE. However, rationale management is not well integrated into CSE. The overall goal of this dissertation is to provide workflows and tool support for continuous rationale management. The dissertation contributes an interview study with practitioners from the industry, which investigates rationale management problems, current practices, and features to support continuous rationale management beneficial for practitioners. Problems of rationale management in practice are threefold: First, documenting decision knowledge is intrusive in the development process and an additional effort. Second, the high amount of distributed decision knowledge documentation is difficult to access and use. Third, the documented knowledge can be of low quality, e.g., outdated, which impedes its use. The dissertation contributes a systematic mapping study on recommendation and classification approaches to treat the rationale management problems. The major contribution of this dissertation is a validated approach for continuous rationale management consisting of the ConRat life cycle model extension and the comprehensive ConDec tool support. To reduce intrusiveness and additional effort, ConRat integrates rationale management activities into existing workflows, such as requirements elicitation, development, and meetings. ConDec integrates into standard development tools instead of providing a separate tool. ConDec enables lightweight capturing and use of decision knowledge from various artifacts and reduces the developers' effort through automatic text classification, recommendation, and nudging mechanisms for rationale management. To enable access and use of distributed decision knowledge documentation, ConRat defines a knowledge model of decision knowledge and other artifacts. ConDec instantiates the model as a knowledge graph and offers interactive knowledge views with useful tailoring, e.g., transitive linking. To operationalize high quality, ConRat introduces the rationale backlog, the definition of done for knowledge documentation, and metrics for intra-rationale completeness and decision coverage of requirements and code. ConDec implements these agile concepts for rationale management and a knowledge dashboard. ConDec also supports consistent changes through change impact analysis. The dissertation shows the feasibility, effectiveness, and user acceptance of ConRat and ConDec in six case study projects in an industrial setting. Besides, it comprehensively analyses the rationale documentation created in the projects. The validation indicates that ConRat and ConDec benefit CSE projects. Based on the dissertation, continuous rationale management should become a standard part of CSE, like automated testing or continuous integration