Titlebook: Cloud Service Benchmarking; Measuring Quality of David Bermbach,Erik Wittern,Stefan Tai Textbook 2017 Springer International Publishing AG

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Data PreprocessingHowever, any analysis efforts will be limited by the quality of the input data. Therefore, in this chapter, we introduce data preprocessing methods that enhance data quality for later analysis steps.We start by outlining the characteristics of cloud benchmarking data, which affect the selection of p

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Using Insights on Cloud Service Qualityhey still need to be used for some purpose. In this chapter, we discuss two ways for leveraging insights from cloud service benchmarking. First, we describe how these insights can be communicated to interested parties. Second, we describe through examples how these insights can drive actions, e.g.,

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Schulorte und Raumgefüge informellen Lernenses and cloud-based systems. Finally, we also discuss how concrete benchmarks may have to compromise one objective to reach another one and describe how the use of cloud services, both as system under test and as experimentation testbed, can influence these objectives.

HARD

Schularchitektur und rituelle Raumpraktikenunning experiments, namely, ensuring that the required resources are readily available when needed. We then dive into addressing challenges that occur directly before, during, and after running an experiment, including challenges associated with collecting benchmarking data, data provenance, and storing data.

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https://doi.org/10.1007/978-3-322-95122-9when deciding on cloud service selection and configuration, or ultimately how to design applications that can either compensate for or leverage particular quality characteristics of underlying services.

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rough the process of designing, implementing and executing a.Cloud service benchmarking can provide important, sometimes surprising insights into the quality of services and leads to a more quality-driven design and engineering of complex software architectures that use such services. Starting with

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