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Auflistung nach Autor:in "Qin, Cui"

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  • Zeitschriftenartikel
    Adaptive Application Performance Management for Big Data Stream Processing
    (Softwaretechnik-Trends Band 35, Heft 3, 2015) Eichelberger, Holger; Qin, Cui; Schmid, Klaus; Niederée, Claudia
    Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without overprovisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.
  • Konferenzbeitrag
    Enactment of Adaptation in Data Stream Processing with Latency Implications
    (Software Engineering 2020, 2020) Qin, Cui; Eichelberger, Holger; Schmid, Klaus
    This summary refers to the paper Enactment of adaptation in data stream processing with latency implications – A systematic literature review. This paper is a journal paper published in Information and Software Technology (IST) in July 2019. Runtime adaptation in stream processing plays a significant role in supporting the optimization of data processing tasks. In recent years, runtime adaptation, particularly its enactment, has received significant interest in scientific literature. However, so far no categorization of the enactment approaches for runtime adaptation in stream processing has been established. This paper presents a systematic literature review (SLR), where we identify and characterize different approaches towards the enactment of runtime adaptation in stream processing with a main focus on latency as quality dimension. We discovered 75 relevant papers out of 244 papers from the search. We identified 17 different enactment categories and developed a taxonomy to characterize all possible enactment approaches. We extracted the realization techniques of each identified enactment approach and classified them into categories. Furthermore, we identified 9 categories of processing problems, 6 adaptation goals, 9 evaluation metrics and 12 evaluation parameters from the identified enactment approaches. The research interest on enactment approaches has significantly increased in recent years. The most commonly applied enactment approaches are parameter adaptation to tune parameters or settings of the processing, load balancing used to re-distribute workloads, and processing scaling to dynamically scale up and down the processing.
  • Konferenzbeitrag
    Experiences with the Model-based Generation of Big Data Pipelines
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Eichelberger, Holger; Qin, Cui; Schmid, Klaus
    Developing Big Data applications implies a lot of schematic or complex structural tasks, which can easily lead to implementation errors and incorrect analysis results. In this paper, we present a model-based approach that supports the automatic generation of code to handle these repetitive tasks, enabling data engineers to focus on the functional aspects without being distracted by technical issues. In order to identify a solution, we analyzed different Big Data stream-processing frameworks, extracted a common graph-based model for Big Data streaming applications and de- veloped a tool to graphically design and generate such applications in a model-based fashion (in this work for Apache Storm). Here, we discuss the concepts of the approach, the tooling and, in particular, experiences with the approach based on feedback of our partners.