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

Model Transformation Languages with Modular Information Hiding

Model transformations, together with models, form the principal artifacts in model-driven software development. Industrial practitioners report that transformations on larger models quickly get sufficiently large and complex themselves. To alleviate entailed maintenance efforts, this thesis presents a modularity concept with explicit interfaces, complemented by software visualization and clustering techniques. All three approaches are tailored to the specific needs of the transformation domain

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Quality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques

Performance Benchmarking of Application Monitoring Frameworks

Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe. While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monitoring. Benchmarks can be used as an effective and affordable way for these evaluations. However, no benchmark specifically targeting the overhead of monitoring itself exists. Furthermore, no established benchmark engineering methodology exists that provides guidelines for the design, execution, and analysis of benchmarks. This thesis introduces a benchmark approach to measure the performance overhead of application-level monitoring frameworks. The core contributions of this approach are 1) a definition of common causes of monitoring overhead, 2) a general benchmark engineering methodology, 3) the MooBench micro-benchmark to measure and quantify causes of monitoring overhead, and 4) detailed performance evaluations of three different application-level monitoring frameworks. Extensive experiments demonstrate the feasibility and practicality of the approach and validate the benchmark results. The developed benchmark is available as open source software and the results of all experiments are available for download to facilitate further validation and replication of the results

Integrated Software Architecture-Based Reliability Prediction for IT Systems

With the increasing importance of reliability in business and industrial IT systems, new techniques for architecture-based software reliability prediction are becoming an integral part of the development process. This dissertation thesis introduces a novel reliability modelling and prediction technique that considers the software architecture with its component structure, control and data flow, recovery mechanisms, its deployment to distributed hardware resources and the system\u27s usage profile

Integrated Software Architecture-Based Reliability Prediction for IT Systems

With the increasing importance of reliability in business and industrial IT systems, new techniques for architecture-based software reliability prediction are becoming an integral part of the development process. This dissertation thesis introduces a novel reliability modelling and prediction technique that considers the software architecture with its component structure, control and data flow, recovery mechanisms, its deployment to distributed hardware resources and the system´s usage profile

Investigating London’s Post Medieval Pipe Clay Figurines From 1500-1800: Critiquing 3D Approaches to Mould Generation Analysis Via English and Transatlantic Case Studies

This thesis has two main strands to its research, one being the first comprehensive synthesis of London’s post-medieval pipe clay figurines dating to the period 1500-1800, combined with examining the potential for inexpensive 3D imaging technology to carry out a new digitised methodology for mould matching and figurine generational analysis. By applying this new digital methodology new insights have been gained on the wider context of these artefacts. The thesis also contextualises the London material with a broad array of academic publications on pipe clay figurines from Britain, Germany, the Netherlands, Poland, Jamaica, and America. This has included an extensive comparison between the previously unappreciated pipe clay figurines from London and figurines from Germany and the Low Countries and a specific comparison with data collected from the United States of America. This compendium of data provides more information to examine a range of questions, such as production, distribution, iconography, intended audience, and the general economic, social, and religious setting in which they operated. By drawing upon these resources and new avenues of research this investigation offers an insight into pipe clay figurines within Germany and the Low Countries by examining a series of archaeological and contemporary literary sources. Following chapters go on to explore both the London and New World assemblages, presenting details on the distribution of these collections, a contextualised discussion on consumer markets, and iconographical relations of specific case studies. It is from this assemblage that figurines presenting similar stylistic qualities were selected for further analysis via 3D imaging methodologies to comprehend how closely, if at all, the morphometrics of the figurines compare and whether these figurines were produced from related mould groups. The parameters for this analysis are developed in Chapters 4 and 6, which discuss controlled datasets and a series of tests investigating the accuracy of inexpensive 3D imaging technology and their suitability for pipe clay figurine 3D imaging. These tests also analysed other potential influences on the morphometrics of the figurines and designed error parameters to be taken into account so that potential mould relationships could still be observed between figurines that had experienced damage, erosion, or manipulated on removal from their mould. These two strands are then brought together in Chapter 8, where new theories are discussed concerning the causes behind the changing iconography of these figurines, particularly those from London and the New World. This thesis also highlights the wider potential of 3D modelling for artefact studies and the limitations of Structure from Motion in the field of mould analysis. Overall, the research covered within this thesis has provided new details on a previously unstudied dataset alongside a much-needed critique of a new technological approach to 3D modelling and a brand new and revitalising means of carrying out mould-matching analysis of artefacts and other archaeological material

Investigating and Writing Achitectural History: Subjects, Methodologies and Frontiers.

The volume contains the abstracts and full texts of the 157 papers and position statements presented and discussed at the III EAHN (European Architectural History) International Meeting, Torino 19-21 June 201