Randomization is a key element in sequential and distributed computing. Reasoning about
randomized algorithms is highly non-trivial. In the 1980s, this initiated first proof methods …

Modern software applications are subject to uncertain operating conditions, such as
dynamics in the availability of services and variations of system goals. Consequently …

A concise and practical introduction to the foundations and engineering principles of self-
adaptation Though it has recently gained significant momentum, the topic of self-adaptation …

Recent years have seen the massive migration of enterprise applications to the cloud. One
of the challenges posed by cloud applications is Quality-of-Service (QoS) management …

Building on concepts drawn from control theory, self-adaptive software handles
environmental and internal uncertainties by dynamically adjusting its architecture and …

Self-adaptive systems tend to be reactive and myopic, adapting in response to changes
without anticipating what the subsequent adaptation needs will be. Adapting reactively can …

Self-adaptation enables software to execute successfully in dynamic, unpredictable, and
uncertain environments. Control theory provides a broad set of mathematically grounded …

The complexity of modern software systems has grown enormously in the past years with
users always demanding for new features and better quality of service. Besides, software is …

We present PROPhESY, a tool for analyzing parametric Markov chains (MCs). It can
compute a rational function (ie, a fraction of two polynomials in the model parameters) for …

This book is the first ever to focus on the emerging field of self-aware computing from an
engineering perspective. It first comprehensively introduces fundamentals for self …