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. 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. 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. This book begins by introducing the concepts and requirements of autonomic computing and exploring the architectures required to implement such a system. 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. 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. 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.

"High Performance Deformable Image Registration Algorithms for Manycore Processors" develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.

This book demonstrates:

How to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithmsHow to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUsProgramming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU