Evaluation Issues
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 computin
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Evaluation Issues
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.
P. Lalanda et al., Autonomic Computing: Principles, Design and Implementation, Undergraduate Topics in Computer Science, DOI 10.1007/978-1-4471-5007-7_8, © Springer-Verlag London 2013
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Evaluation Issues
Evaluating Autonomic Systems
We can assume that an autonomic system consists of a number of components that interact with each other and their environment. There may be a single autonomic manager that governs a large part of the system, or it may consist of a number of more localised managers that make decisions that emerge a global solution to maintain a goal (see Chap. 4). Either way, an autonomic manager must adapt to stimuli that originate from both within the system it governs and from outside that system (what we called its context in Chap. 2). Kaddoum et al. [1] describe these sources of stimuli as the dynamics that come from exogenous or endogenous changes. Endogenous changes are perturbations caused by entities within the system, whereas exogenous disturbances originate outside the system. As discussed in previous chapters, autonomic features can be either designed as part of a new system build or retrofitted to a current or legacy computing system. The reasons for this addition are to ensure that the system meets certain goals, either more efficiently or more robustly or in a more cost-effective way. In designing an autonomic system, the aim is to have an operational system that essentially is able to reach a stable state, that is, to reach homeostasis. Homeostasis is an indicator of how well the ideal (or acceptable) state, as defined by the system goals, can be maintained given the exogenous or endogenous stimuli that act upon it. That is, it is a measure of how well the system can return to a stable state when disturbances, faults or perturbations have occurred. It is already difficult to design a true evaluation for general computer systems, whether it is to understand the behaviour of a given system or to compare versions of the same system in terms of core function
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