Dependent Models

Despite means of fault prevention such as extensive testing or formal veri?cation, errors inevitably occur during system operation. To avoid subsequent system fa- ures, critical distributed systems, therefore, require engineering of means for fault tolerance. Achieving fault tolerance requires some redundancy, which, unfor- nately, is bound to limitations. Appropriate fault models are needed to describe which types of faults and how many faults are tolerable in a certain context. Pre- ous research on distributed systems has often introduced fault models that abstract too many relevant system properties such as dependent and propagating com- nent failures. In this research work, Timo Warns introduces new structural failure models that are both accurate (to cover relevant properties) and tractable (to be - alyzable). These new failure models cover dependent failures (for instance, failure correlation by geographic proximity) and propagating failures (for instance, pr- agation by service utilization). To evaluate the new failure models, Timo Warns shows how some seminal problems in distributed systems can be solved with - proved resilience and ef?ciency, as compared to existing solutions. Particularly, the textbook-style introduction to distributed systems and the r- orous presentation of the new failure models and their evaluation may serve as an example for other software engineering research projects – which is why this book is a valuable addition to both a researcher’s and a student’s library.

A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression models, multivariate regression models, and simultaneous equations models. Methods are illustrated with worked examples, complete with figures that display code and output.