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 through supplying them with state of the art examine actions and destiny possibilities and traits. The contributions made through sought after researchers from academia and comprise cutting-edge architectures, protocols, applied sciences, and purposes in pervasive computing and instant networking. particularly, the e-book covers present and rising communications and computing versions, layout architectures, cellular and instant functions, know-how and study matters in autonomic computing platforms and communications.
Important gains include:
- A entire reference on autonomic computing and networking,
- State-of-the-art concepts utilized in the field,
- Illustrative figures permitting effortless examining, and
- Emerging tendencies and open examine difficulties and matters.
The fabric is geared up into sections: autonomic computing and autonomic networking. With a complete of eighteen chapters, every one part comprises 9 chapters addressing current and rising architectures, protocols, and functions.
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Extra resources for Autonomic Computing and Networking
For example, systems in the MetaH style supports real-time schedulability analysis . For self-adaptation, given some quality objectives, each style may guide the choice of system properties to monitor, help identify strategic points for system observation, and suggest possible adaptations. To illustrate this, consider a signalprocessing system with an architecture in the pipe-ﬁlter style. This style constrains the system to a data-flow computation pattern, points to throughput as a system property, identifies the ﬁlter as a strategic point for measuring throughput, and suggests throughput analysis for reasoning about overall system throughput.
However, automating decisions that trade off multiple objectives to adapt a system is still unsupported in most systems today. , load balancing), it is much harder to do so in the presence of conflicting qualities such as security. com-customized Model Manager. The customized Architecture Evaluator evaluates the model to make sure that no client experiences a request-response latency above a certain threshold. If a client is experiencing above-threshold latencies, the Evaluator triggers the Adaptation Manager to initiate the adaptation process and determine whether to activate more servers or decrease content quality.
J. Strassner et al. Providing seamless mobility using the FOCALE autonomic architecture. In Proc. 7th Intl. Conf. Next Generation Teletraffic and Wired/Wireless Advanced Networking, volume 4712 of LNCS, pages 330–341, 2007. 43. J. Strassner et al. Ontologies in the engineering of management and autonomic systems: A reality check. Journal of Network and Systems Management, 15(1):5–11, 2007. 44. E. Walsh et al. Utility functions in autonomic systems. In Proc. 1st Intl. Conf. Autonomic Computing, pages 70–77, 2004.