By Radu Calinescu (auth.), Yan Zhang, Laurence Tianruo Yang, Mieso K. Denko (eds.)
Autonomic Computing and Networking serves as a technical consultant and reference fabric for engineers, scientists, practitioners, and researchers via supplying them with state of the art learn actions and destiny possibilities and tendencies. The contributions made by way of admired researchers from academia and contain state of the art architectures, protocols, applied sciences, and purposes in pervasive computing and instant networking. particularly, the booklet covers current and rising communications and computing types, layout architectures, cellular and instant functions, know-how and learn matters in autonomic computing structures and communications.
Important positive aspects include:
- A accomplished reference on autonomic computing and networking,
- State-of-the-art strategies utilized in the field,
- Illustrative figures permitting effortless examining, and
- Emerging traits and open learn difficulties and concerns.
The fabric is prepared into sections: autonomic computing and autonomic networking. With a complete of eighteen chapters, each one part includes 9 chapters addressing latest and rising architectures, protocols, and functions.
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Additional info for Autonomic Computing and Networking
We present its design and customization points in Sect. 3. Next, we discuss control systems theory, which is integral to the design of our self-adaptation framework. 2 Control Systems and the Self-Adaptation Cycle The second major element of Rainbow is the application of control systems concepts to the adaptation problem. Self-adaptation requires a closed loop of control. We choose a specific type of control system model to make our approach generalizable and reusable across diffeent classes of system.
TargetAvailability)), true, f alse)). 6 Section 8 suggests techniques for working around the time taken by runtime model checking when such delays are not acceptable. General-Purpose Autonomic Computing 25 Fig. 14 Autonomic system of systems comprising several instances of the data-centre system from Sect. 4, and an autonomic-enabled web page implementing a business dashboard. The data-centre systems were each configured to expose their actual and ideal utility by means of a resourcedefinition policy, and the top-level policy engine implements an action policy that updates the properties of the autonomic-enabled web page with a summary of these utilities As described in Sect.
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.