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

A Careers Perspective on Entrepreneurship

[Excerpt] What if being an entrepreneur were treated like any other occupation—teacher, nurse, manager? What if the decision to found a new venture were thought of as one of many options that individuals consider as they try to structure a meaningful and rewarding career? How would the field of entrepreneurship research be different? In our view, there is much to be learned by conceiving of entrepreneurship not solely as a final destination, but as a step along a career trajectory. Doing so opens the study of entrepreneurship to a wider range of scholarly insights, and promises important insights for entrepreneurial practice, training, and policy. This special issue takes an important step in this direction

Frequency-dependent and correlational selection pressures have conflicting consequences for assortative mating in a color-polymorphic lizard, Uta stansburiana

Acknowledgments We would like to thank the numerous undergraduate researchers involved with this project for their invaluable assistance in lizard rearing and data collection. We also thank D. Haisten, A. Runemark, Y. Takahashi, and M. Verzijden for insightful comments on the manuscript. This project was funded by National Science Foundation DEBOS-15973 to A.G.M. and B.R.S.Peer reviewedPublisher PD

To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance

Self-adaptive system (SAS) can adapt itself to optimize various key performance indicators in response to the dynamics and uncertainty in environment. In this paper, we present Debt Learning Driven Adaptation (DLDA), an framework that dynamically determines when and whether to adapt the SAS at runtime. DLDA leverages the temporal adaptation debt, a notion derived from the technical debt metaphor, to quantify the time-varying money that the SAS carries in relation to its performance and Service Level Agreements. We designed a temporal net debt driven labeling to label whether it is economically healthier to adapt the SAS (or not) in a circumstance, based on which an online machine learning classifier learns the correlation, and then predicts whether to adapt under the future circumstances. We conducted comprehensive experiments to evaluate DLDA with two different planners, using 5 online machine learning classifiers, and in comparison to 4 state-of-the-art debt- oblivious triggering approaches. The results reveal the effectiveness and superiority of DLDA according to different metrics

Oral application of L-menthol in the heat: From pleasure to performance

When menthol is applied to the oral cavity it presents with a familiar refreshing sensation and cooling mint flavour. This may be deemed hedonic in some individuals, but may cause irritation in others. This variation in response is likely dependent upon trigeminal sensitivity toward cold stimuli, suggesting a need for a menthol solution that can be easily personalised. Menthol’s characteristics can also be enhanced by matching colour to qualitative outcomes; a factor which can easily be manipulated by practitioners working in athletic or occupational settings to potentially enhance intervention efficacy. This presentation will outline the efficacy of oral menthol application for improving time trial performance to date, either via swilling or via co-ingestion with other cooling strategies, with an emphasis upon how menthol can be applied in ecologically valid scenarios. Situations in which performance is not expected to be enhanced will also be discussed. An updated model by which menthol may prove hedonic, satiate thirst and affect ventilation will also be presented, with the potential performance implications of these findings discussed and modelled. Qualitative reflections from athletes that have implemented menthol mouth swilling in competition, training and maximal exercise will also be included

Why study movement variability in autism?

Autism has been defined as a disorder of social cognition, interaction and communication where ritualistic, repetitive behaviors are commonly observed. But how should we understand the behavioral and cognitive differences that have been the main focus of so much autism research? Can high-level cognitive processes and behaviors be identified as the core issues people with autism face, or do these characteristics perhaps often rather reflect individual attempts to cope with underlying physiological issues? Much research presented in this volume will point to the latter possibility, i.e. that people on the autism spectrum cope with issues at much lower physiological levels pertaining not only to Central Nervous Systems (CNS) function, but also to peripheral and autonomic systems (PNS, ANS) (Torres, Brincker, et al. 2013). The question that we pursue in this chapter is what might be fruitful ways of gaining objective measures of the large-scale systemic and heterogeneous effects of early atypical neurodevelopment; how to track their evolution over time and how to identify critical changes along the continuum of human development and aging. We suggest that the study of movement variability—very broadly conceived as including all minute fluctuations in bodily rhythms and their rates of change over time (coined micro-movements (Figure 1A-B) (Torres, Brincker, et al. 2013))—offers a uniquely valuable and entirely objectively quantifiable lens to better assess, understand and track not only autism but cognitive development and degeneration in general. This chapter presents the rationale firstly behind this focus on micro-movements and secondly behind the choice of specific kinds of data collection and statistical metrics as tools of analysis (Figure 1C). In brief the proposal is that the micro-movements (defined in Part I – Chapter 1), obtained using various time scales applied to different physiological data-types (Figure 1), contain information about layered influences and temporal adaptations, transformations and integrations across anatomically semi-independent subsystems that crosstalk and interact. Further, the notion of sensorimotor re-afference is used to highlight the fact that these layered micro-motions are sensed and that this sensory feedback plays a crucial role in the generation and control of movements in the first place. In other words, the measurements of various motoric and rhythmic variations provide an access point not only to the “motor systems”, but also access to much broader central and peripheral sensorimotor and regulatory systems. Lastly, we posit that this new lens can also be used to capture influences from systems of multiple entry points or collaborative control and regulation, such as those that emerge during dyadic social interactions

Technology evaluation of man-rated acceleration test equipment for vestibular research

The considerations for eliminating acceleration noise cues in horizontal, linear, cyclic-motion sleds intended for both ground and shuttle-flight applications are addressed. the principal concerns are the acceleration transients associated with change in direction-of-motion for the carriage. The study presents a design limit for acceleration cues or transients based upon published measurements for thresholds of human perception to linear cyclic motion. The sources and levels for motion transients are presented based upon measurements obtained from existing sled systems. The approaches to a noise-free system recommends the use of air bearings for the carriage support and moving-coil linear induction motors operating at low frequency as the drive system. Metal belts running on air bearing pulleys provide an alternate approach to the driving system. The appendix presents a discussion of alternate testing techniques intended to provide preliminary type data by means of pendulums, linear motion devices and commercial air bearing tables

Effects of remote limb ischemic conditioning on muscle strength in healthy young adults: A randomized controlled trial

Remote limb ischemic conditioning (RLIC) is a clinically feasible method in which brief, sub-lethal bouts of ischemia protects remote organs or tissues from subsequent ischemic injury. A single session of RLIC can improve exercise performance and increase muscle activation. The purpose of this study, therefore, was to assess the effects of a brief, two-week protocol of repeated RLIC combined with strength training on strength gain and neural adaptation in healthy young adults. Participants age 18-40 years were randomized to receive either RLIC plus strength training (n = 15) or sham conditioning plus strength training (n = 15). Participants received RLIC or sham conditioning over 8 visits using a blood pressure cuff on the dominant arm with 5 cycles of 5 minutes each alternating inflation and deflation. Visits 3-8 paired conditioning with wrist extensors strength training on the non-dominant (non-conditioned) arm using standard guidelines. Changes in one repetition maximum (1 RM) and electromyography (EMG) amplitude were compared between groups. Both groups were trained at a similar workload. While both groups gained strength over time (P = 0.001), the RLIC group had greater strength gains (9.38 ± 1.01 lbs) than the sham group (6.3 ± 1.08 lbs, P = 0.035). There was not a significant group x time interaction in EMG amplitude (P = 0.231). The RLIC group had larger percent changes in 1 RM (43.8% vs. 26.1%, P = 0.003) and EMG amplitudes (31.0% vs. 8.6%, P = 0.023) compared to sham conditioning. RLIC holds promise for enhancing muscle strength in healthy young and older adults, as well as clinical populations that could benefit from strength training