By Irene Eusgeld, Felix C. Freiling (auth.), Irene Eusgeld, Felix C. Freiling, Ralf Reussner (eds.)
With the transforming into ubiquity of computing structures, it truly is crucial that we will be able to depend on the prone they carry. Justifying reliance in computers calls for medical recommendations to derive proof from given structures or are expecting such proof. This educational publication provides an outline of the present cutting-edge in measuring the various facets of dependability of structures: reliability, safeguard and performance.
The major impulse for this dependability metrics venture resulted from a learn seminar, held at Schloss Dagstuhl, Germany, in October/November 2005. The 25 chapters, in response to the end result of the study seminar, are prepared in 5 elements on foundations, reliability metrics, defense metrics, functionality metrics, and overlapping metrics. the ultimate half indicates that just a mixed attention of significant dependability attributes will bring about the layout aim: the advance of platforms we will fairly trust.
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Additional resources for Dependability Metrics: Advanced Lectures
Al-Ali et al.  defines a language of QoS policies for grid services that are enforced by means of adaptation mechanisms. A different approach to QoS adaptation is considered in Sharma et al.  for the case of embedded systems. These systems do not involve forecasting of values as part of their adaptation strategies. The ARIMA methods need not only be applied to compute QoS queue characteristics. These strategies have the Consistent Metric Usage: From Design to Deployment 33 potential to be combined with such (non-queued) QoS-based runtime adaptation technologies.
Recall that empirical metrics can only make an empirically measurable observation over some aspect of the system and offer no recourse to model-based prediction. In software engineering, the most obvious empirical metrics are syntactic measures over an implementation: for example, a code documentation measure. There are also metrics that specifically concern the design – such as conformance to a standard, or the degree of completeness of specification. These metrics also do not result in a validation problem, obviously, as they are never predicted and are useful only when we can immediately measure them.
For example, the delay caused by a service call varies over time. Hence, the delay can only be characterized using statistical means. QML supports four different types of stochastical information. Mean and variance are characteristic attributes of probability density functions. Percentiles and frequencies describe probability distribution functions in more detail. An explanation of these basic statistical concepts can be found in basic statistic literature and is omitted here. An example is depicted in code fragment 5 (units are omitted).