Abstract
Computer scientists, and the computing industries, rely on the ability to build systems and iteratively evaluate the design and implementational decisions that they have made during that process. As we have seen in previous chapters, an autonomic computing system can take many forms and as a consequence their evaluation, and moreover comparison, can be difficult. The very nature of some systems that emerge solutions adds further complexity to their evaluation. This chapter presents the challenges to evaluating an autonomic system, what to look out for and what others have attempted to do to aid this activity.
The chapter’s aim is to enable the reader to be able to design tests and metrics that can be used to evaluate autonomic computing systems with a particular focus on the aspects that makes an autonomic system different from those without self-management features. As you will see, there is no single definitive metric that can be used in assessing the mechanisms of all autonomic computing systems.
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Lalanda, P., McCann, J.A., Diaconescu, A. (2013). Evaluation Issues. In: Autonomic Computing. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5007-7_8
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DOI: https://doi.org/10.1007/978-1-4471-5007-7_8
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