In this chapter, we propose a list ofmetrics grouped by theMAPE-K paradigm for quantifying properties of self-aware computing systems. This set of metrics can be seen as a starting point toward benchmarking and comparing self-aware computing systems on a level-playing field.We discuss state-of-the art approaches in the related fields of self-adaptation and self-protection to identify commonalities in metrics for self-aware computing. We illustrate the need for benchmarking self-aware computing systems with the help of an approach that uncovers real-time characteristics of operating systems. Gained insights of this approach can be seen as a way of enhancing self-awareness by a measurement methodology on an ongoing basis. At the end of this chapter, we address new challenges in reference workload definition for benchmarking self-aware computing systems, namely load intensity patterns and burstiness modeling.
CITATION STYLE
Herbst, N., Becker, S., Kounev, S., Koziolek, H., Maggio, M., Milenkoski, A., & Smirni, E. (2017). Metrics and benchmarks for self-aware computing systems. In Self-Aware Computing Systems (pp. 437–464). Springer International Publishing. https://doi.org/10.1007/978-3-319-47474-8_14
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