358 research outputs found
ENHANCING CLOUD SYSTEM RUNTIME TO ADDRESS COMPLEX FAILURES
As the reliance on cloud systems intensifies in our progressively digital world, understanding and reinforcing their reliability becomes more crucial than ever. Despite impressive advancements in augmenting the resilience of cloud systems, the growing incidence of complex failures now poses a substantial challenge to the availability of these systems. With cloud systems continuing to scale and increase in complexity, failures not only become more elusive to detect but can also lead to more catastrophic consequences. Such failures question the foundational premises of conventional fault-tolerance designs, necessitating the creation of novel system designs to counteract them.
This dissertation aims to enhance distributed systemsâ capabilities to detect, localize, and react to complex failures at runtime. To this end, this dissertation makes contributions to address three emerging categories of failures in cloud systems. The first part delves into the investigation of partial failures, introducing OmegaGen, a tool adept at generating tailored checkers for detecting and localizing such failures. The second part grapples with silent semantic failures prevalent in cloud systems, showcasing our study findings, and introducing Oathkeeper, a tool that leverages past failures to infer rules and expose these silent issues. The third part explores solutions to slow failures via RESIN, a framework specifically designed to detect, diagnose, and mitigate memory leaks in cloud-scale infrastructures, developed in collaboration with Microsoft Azure. The dissertation concludes by offering insights into future directions for the construction of reliable cloud systems
Examining the Relationships Between Distance Education Studentsâ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between studentsâ self-efficacy (SSE) and studentsâ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievementâphysical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievementâmore time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Capturing and Scaffolding the Complexities of Self-Regulation During Game-Based Learning
Game-based learning environments (GBLEs) can offer students with engaging interactive instructional materials while also providing a research platform to investigate the dynamics and intricacies of effective self-regulated learning (SRL). Past research has indicated learners are often unable to monitor and regulate their cognitive and metacognitive processes within GBLEs accurately and effectively on their own due mostly to the open-ended nature of these environments. The future design and development of GBLEs and embedded scaffolds, therefore, require a better understanding of the discrepancies between the affordances of GBLEs and the required use of SRL. Specifically, how to incorporate interdisciplinary theories and concepts outside of traditional educational, learning, and psychological sciences literature, how to utilize process data to measure SRL processes during interactions with instructional materials accounting for the dynamics of leaners\u27 SRL, and how to improve SRL-driven scaffolds to be individualized and adaptive based on the level of agency GBLEs provide. Across four studies, this dissertation investigates learners\u27 SRL while they learn about microbiology using CRYSTAL ISLAND, a GBLE, building upon each other by enhancing the type of data collected, analytical methodologies used, and applied theoretical models and theories. Specifically, this dissertation utilizes a combination of traditional statistical approaches (i.e., linear regression models), non-linear statistical approaches (i.e., growth modeling), and non-linear dynamical theory (NDST) approaches (aRQA) with process trace data to contribute to the field\u27s current understanding of the dynamics and complexities of SRL. Furthermore, this dissertation examines how limited agency can act as an implicit scaffold during game-based learning to promote the use of SRL processes and increase learning outcomes
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Utilizing Runtime Information for Accurate Root Cause Identification in Performance Diagnosis
This dissertation highlights that existing performance diagnostic tools often become less effective due to their inherent inaccuracies in modern software. To overcome these inaccuracies and effectively identify the root causes of performance issues, it is necessary to incorporate supplementary runtime information into these tools. Within this context, the dissertation integrates specific runtime information into two typical performance diagnostic tools: profilers and causal tracing tools.
The integration yields a substantial enhancement in the effectiveness of performance diagnosis. Among these tools, gprof stands out as a representative profiler for performance diagnosis. Nonetheless, its effectiveness diminishes as the time cost calculated based on CPU sampling fails to accurately and adequately pinpoint the root causes of performance issues in complex software. To tackle this challenge, the dissertation introduces an innovative methodology called value-assisted cost profiling (vProf). This approach incorporates variable values observed during runtime into the profiling process.
By continuously sampling variable values from both normal and problematic executions, vProf refines function cost estimates, identifies anomalies in value distributions, and highlights potentially problematic code areas that could be the actual sources of performance is- sues. The effectiveness of vProf is validated through the diagnosis of 18 real-world performance is- sues in four widely-used applications. Remarkably, vProf outperforms other state-of-the-art tools, successfully diagnosing all issues, including three that had remained unresolved for over four years.
Causal tracing tools reveal the root causes of performance issues in complex software by generating tracing graphs. However, these graphs often suffer from inherent inaccuracies, characterized by superfluous (over-connected) and missed (under-connected) edges. These inaccuracies arise from the diversity of programming paradigms. To mitigate the inaccuracies, the dissertation proposes an approach to derive strong and weak edges in tracing graphs based on the verticesâ semantics collected during runtime. By leveraging these edge types, a beam-search-based diagnostic algorithm is employed to identify the most probable causal paths. Causal paths from normal and buggy executions are differentiated to provide key insights into the root causes of performance issues. To validate this approach, a causal tracing tool named Argus is developed and tested across multiple versions of macOS. It is evaluated on 12 well-known spinning pinwheel issues in popular macOS applications. Notably, Argus successfully diagnoses the root causes of all identified issues, including 10 issues that had remained unresolved for several years.
The results from both tools exemplify a substantial enhancement of performance diagnostic tools achieved by harnessing runtime information. The integration can effectively mitigate inherent inaccuracies, lend support to inaccuracy-tolerant diagnostic algorithms, and provide key insights to pinpoint the root causes
Changing Priorities. 3rd VIBRArch
In order to warrant a good present and future for people around the planet and to safe the care of the planet itself, research in architecture has to release all its potential. Therefore, the aims of the 3rd Valencia International Biennial of Research in Architecture are:
- To focus on the most relevant needs of humanity and the planet and what architectural research can do for solving them.
- To assess the evolution of architectural research in traditionally matters of interest and the current state of these popular and widespread topics.
- To deepen in the current state and findings of architectural research on subjects akin to post-capitalism and frequently related to equal opportunities and the universal right to personal development and happiness.
- To showcase all kinds of research related to the new and holistic concept of sustainability and to climate emergency.
- To place in the spotlight those ongoing works or available proposals developed by architectural researchers in order to combat the effects of the COVID-19 pandemic.
- To underline the capacity of architectural research to develop resiliency and abilities to adapt itself to changing priorities.
- To highlight architecture's multidisciplinarity as a melting pot of multiple approaches, points of view and expertise.
- To open new perspectives for architectural research by promoting the development of multidisciplinary and inter-university networks and research groups.
For all that, the 3rd Valencia International Biennial of Research in Architecture is open not only to architects, but also for any academic, practitioner, professional or student with a determination to develop research in architecture or neighboring fields.Cabrera Fausto, I. (2023). Changing Priorities. 3rd VIBRArch. Editorial Universitat Politècnica de València. https://doi.org/10.4995/VIBRArch2022.2022.1686
Bayesian network analysis of software logs for data-driven software maintenance
Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data-driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.Junta de AndalucĂa, Grant/Award Number: P20â00091; AEI, Grant/Award Number: PID2019â106758GBâ32/AEI/10.13039/501100011033; Spanish project, Grant/Award Number: PDC2021â121195âI00; Spanish Program, Grant/Award Number: BEAGAL18/00064Peer ReviewedPostprint (published version
Identifying and transforming sites of power in collaborative community-based research
For this dissertation, I analyzed collaboration practices and power structures within three community-based participatory research (CBPR) studies I conducted for my Ph.D. I ask: 1) How do dominant power structures, epistemologies, and narratives manifest in HCI research and praxis? 2) How can we structure research to support our community partners\u27 goals while resisting dominating and extractive practices in academic research? To respond to these questions, I conducted member checking interviews with my collaborators and a duoethnography with my dissertation advisor, Dr. Sheena Erete, about our experiences in the studies as a Black female professor and a white female graduate student. I grounded my findings in Black feminist thought by employing the intersectional analysis method developed by Erete, Rankin, and Thomas (2022).
Through my intersectional analysis, I identified how systems of power and disciplinary norms influenced Dr. Erete\u27s and my decisions about how to structure our collaborations and organize our time and labor. These decisions impacted the distribution of benefit and harm within our collaborations. Systems of power also manifested in cultural narratives imbued within the studies; these narratives informed our methods and interactions with our collaborators and community members. I organize these findings into five saturated sites of power (a term developed by Collins, 2019) within CBPR. These are sites where intersecting systems of power acutely impact collaborators\u27 experiences and study outcomes. To support researchers in developing a non-extractive and mutually beneficial CBPR practice, I offer a set of reflexive prompts that address three themes: 1) evaluating researchersâ capacity for the work; 2) distributing resources through CBPR; and 3) using narratives as a reflexive tool. This dissertation contributes to critical human-computer interaction (HCI) literature and offers recommendations that researchers can use to intentionally co-design studies that mitigate harm and advance community-defined goals
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