Autonomic Computing: Principles, Design and Implementation (Undergraduate Topics in Computer Science)

Autonomic Computing: Principles, Design and Implementation (Undergraduate Topics in Computer Science)

Philippe Lalanda, Ada Diaconescu

Language: English

Pages: 288

ISBN: 1447150066

Format: PDF / Kindle (mobi) / ePub

This textbook provides a practical perspective on autonomic computing. Through the combined use of examples and hands-on projects, the book enables the reader to rapidly gain an understanding of the theories, models, design principles and challenges of th

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Implementation10.1007/978-1-4471-5007-7_7© Springer-Verlag London 2013 7. The Decision Function Philippe Lalanda1 , Julie A. McCann2 and Ada Diaconescu3 (1)Laboratoire Informatique de Grenoble, Université Joseph Fourier, Grenoble, France (2)Department of Computing, Imperial College London, London, UK (3)Department of Computing and Networking, Télécom ParisTech, Paris, France Abstract In the previous chapters, we saw how self-managed systems could accumulate information about their

characterised by a current state, captured via touchpoints, and one or several possible target states. The problem is then to find out a path from the current state to a satisfactory state. In the context of autonomic computing, a search problem can be defined by: The description of the initial state, corresponding to the current situation of the managed artefacts. The description of the acceptable target states (the goals), also in terms of the managed artefacts. The set of actions that can

that complies with high-level policies). Besides self-* properties, context awareness specifically represents an additional key capability of an autonomic system. Namely, an autonomic system must be able to detect and adapt to changes in its execution environment. This may include user behaviour, available resources or interactions with neighbouring systems. A context-­sensitive system may improve its provided services based on knowledge about service contexts. For example, it can adapt

This undesirable phenomenon is also known as state flapping that occurs particularly in complex systems such as networks (we describe this phenomenon in more detail later on). Such stateful approaches also permit some self-learning, as previously introduced. A deliberative manager can learn from its previous decisions and actions by continuously evaluating and potentially modifying itself. Such capability is, of course, more costly as it requires more resources than reflex-based solutions. This

authentication through the Access Manager back to the browser. In this example, the architecture is modelled, at a level of abstraction that shows the interactions between components; see Fig. 5.1. They then move down a level of abstraction in the hierarchy to focus on these interactions because this is where the potential for failure resides, for example, a broken connection, an incorrect start-up sequence of runnables or excessive heap usage. Therefore in this example, the monitoring task will

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