![]() However, the very complexity inherent in many systems that lend themselves well to autonomic computing can often cause difficulty in designing those same autonomic systems. Collecting cutting-edge work and perspectives from leading experts, Autonomic Computing: Concepts, Infrastructure, and Applications reveals the progress made and outlines the future challenges still facing this exciting and dynamic field.Īutonomic computing is an emerging field for developing complex large-scale systems by transforming them into self-managing autonomic systems, which are intrinsically intended to reduce complexity through automation. In the final section, examples of real-world implementations reflect the potential of emerging autonomic systems, such as dynamic server allocation and runtime reconfiguration and repair. The focus then shifts to the approaches and infrastructures, including control-based and recipe-based concepts, followed by enabling systems, technologies, and services proposed for achieving a set of "self-*" properties, including self-configuration, self-healing, self-optimization, and self-protection. This book begins by introducing the concepts and requirements of autonomic computing and exploring the architectures required to implement such a system. ![]() ![]() Surveying the current path toward this paradigm, Autonomic Computing: Concepts, Infrastructure, and Applications offers a comprehensive overview of state-of-the-art research and implementations in this emerging area. Taking yet another page from the biomimetics playbook, the autonomic computing paradigm mimics the human autonomic nervous system to free system developers and administrators from performing and overseeing low-level tasks. The complexity of modern computer networks and systems, combined with the extremely dynamic environments in which they operate, is beginning to outpace our ability to manage them. This process oriented description is directly amenable to formal verification, as we show here by means of model checking. As the learning technique can be tailored to different abstraction levels according what behavioural primitives we decide to observe, we show and discuss different alternative learned models. This way we show that Behavioural Mining, that extracts directly analyzable behavioural models from other artifacts (specifications or code) is a practicable and very simple way to obtain a process-oriented description of third-party systems. We investigate the conformance of three structurally different types of specification of the case study: (1) a formal specification given in ASSL, (2) a derived implementation in Java, and (3) two behavioral models, one derived from the ASSL specification and one learned from the Java implementation. We revisit our case study on the NASA's Voyager space mission to automatically discover its behaviour by means of model transformation and automata learning. We present this approach along with a simulation case study where ASSL is used to develop control software for the wide-angle camera carried on board NASA's Voyager II Spacecraft. Hardware is sensed via special metrics intended to drive events and self-management policies that help the system handle critical situations in an autonomous reactive manner. ![]() ASSL exposes a rich set of specification constructs that help developers specify event-driven embedded systems. In our approach, we use the ASSL (autonomic system specification language) framework as a development environment, where self-management features of embedded systems are specified and an implementation is automatically generated. In this paper, we present a formal approach to specifying embedded systems capable of self-management. Here, the biggest challenge is still the question how to properly develop and verify such systems. The idea behind computer systems capable of self-management is a complex concept compound by many aspects related to both artificial intelligence and awareness. SUMMARY The increasing complexity of contemporary embedded computing systems requires the use of self-management in order to handle unforeseen changes in both hardware and control software.
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