By Radu Calinescu (auth.), Yan Zhang, Laurence Tianruo Yang, Mieso K. Denko (eds.)
Autonomic Computing and Networking serves as a technical advisor and reference fabric for engineers, scientists, practitioners, and researchers via delivering them with state of the art study actions and destiny possibilities and traits. The contributions made by way of trendy researchers from academia and contain cutting-edge architectures, protocols, applied sciences, and purposes in pervasive computing and instant networking. specifically, the e-book covers latest and rising communications and computing versions, layout architectures, cellular and instant purposes, know-how and study matters in autonomic computing platforms and communications.
Important good points include:
- A complete reference on autonomic computing and networking,
- State-of-the-art thoughts utilized in the field,
- Illustrative figures allowing effortless analyzing, and
- Emerging traits and open learn difficulties and matters.
The fabric is equipped into sections: autonomic computing and autonomic networking. With a complete of eighteen chapters, every one part comprises 9 chapters addressing present and rising architectures, protocols, and purposes.
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Additional info for Autonomic Computing and Networking
G. Kaiser et al. Kinesthetics extreme: An external infrastructure for monitoring distributed legacy systems. In Proc. of the 5th Annual Intl. Active Middleware Workshop, June 2003. 20. H. Kasinger and B. Bauer. Towards a model-driven software engineering methodology for organic computing systems. In Proc. 4th Intl. Conf. Comput. , pages 141–146, 2005. 21. O. M. Chess. The vision of autonomic computing. IEEE Computer Journal, 36(1):41–50, January 2003. 22. S. Kikuchi et al. Policy verification and validation framework based on model checking approach.
One important concern is the uncertainty in the outcome of a particular strategy: enacting a strategy does not necessarily mean that the strategy will be successful on the system. This uncertainty is due to a number of factors, including intervening operation of the system between problem detection and adaptation, inadequate knowledge of the environment, or unanticipated errors in strategy execution. We address this by combining utility theory with a stochastic model of the strategy outcomes. This provides a method to quantify strategies relative to the objectives, under uncertainty.
Challenges and best practices in policy-based autonomic architectures. In Proc. 3rd IEEE Intl. Symp. Dependable, Autonomic and Secure Computing, pages 65–74, 2007. 5. R. Calinescu. Model-driven autonomic architecture. In Proc. 4th IEEE Intl. Conf. Autonomic Computing, June 2007. 6. R. Calinescu. Towards a generic autonomic architecture for legacy resource management. In K. Elleithy, editor, Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering, pages 410–415, Springer, 2008.