Autonomic computing systems must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Clearly, decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper describes the design, implementation, and evaluation of MESO, a pattern classifier designed to support online, incremental learning and decision making in autonomic systems. A novel feature of MESO is its use of small agglomerative clusters, called sensitivity spheres, that aggregate similar training samples. Sensitivity spheres are partitioned into sets during the construction of a memory-efficient hierarchical data structure. This structure facilitates data compression, which is important to many autonomic systems. Results are presented demonstrating that MESO achieves high accuracy while enabling rapid incremental training and classification. A case study is described in which MESO enables a mobile computing application to learn, by imitation, user preferences for balancing wireless network packet loss and bandwidth consumption. Once trained, the application can autonomously adjust error control parameters as needed while the user roams about a wireless cell

Abstract This paper describes the internal architecture and operation of an adaptable communication component called the MetaSocket. MetaSockets are created using Adaptive Java, a reflective extension to Java that enables a component's internal architecture and behavior to be adapted at runtime in response to external stimuli. This paper describes how adaptive behavior is implemented in MetaSockets, as well as how MetaSockets interact with other adaptive components, such as decision makers and event mediators. Results of experiments on a mobile computing testbed demonstrate how MetaSockets respond to dynamic wireless channel conditions in order to improve the quality of interactive audio streams delivered to iPAQ handheld computers. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents the results of an investigation into the efficient implementation of multicast operations for cluster-based parallel computing on asynchronous transfer mode (ATM) networks. Both software- and hardware-based multicast operations have been implemented and studied on a three-switch ATM network testbed. Performance measurements are presented that illustrate how software approaches can best take advantage of switch-based network architectures, and what additional advantage can be gained from using underlying hardware support.>