Why Computer-Based Systems Should be Autonomic

The objective of this paper is to discuss why computer-based systems should be autonomic, where autonomicity implies self-managing, often conceptualized in terms of being self-configuring, self-healing, self-optimizing, self-protecting and self-aware. We look at motivations for autonomicity, examine how more and more systems are exhibiting autonomic behavior, and finally look at future directions

Autonomic Management of Cloud Neighbourhoods through Pulse Monitoring

Abstract-This paper reports on autonomic computing research, including the development of a self-* proof of concept, for a cloud based environment. It monitors administrative boundaries from within an autonomic manager, with each manager operating in a peer-to-peer mode and utilizing a pulse monitor. The prototype was developed in Java utilizing SNMP to demonstrate the manager's self-situation and environment-awareness of the current state of the whole neighborhood and proves the feasibility of communicating the health of the neighborhood to peer managers using an XML pulse concept. Each manager houses the functionality to enact changes to their neighborhood using SNMP based rules. This enables the capability to provide self-healing, self-configuring, self-optimizing and self-protection to network neighborhoods within cloud computing

Biologically-Inspired Concepts for Self-Management of Complexity

Inherent complexity in large-scale applications may be impossible to eliminate or even ameliorate despite a number of promising advances. In such cases, the complexity must be tolerated and managed. Such management may be beyond the abilities of humans, or require such overhead as to make management by humans unrealistic. A number of initiatives inspired by concepts in biology have arisen for self-management of complex systems. We present some ideas and techniques we have been experimenting with, inspired by lesser-known concepts in biology that show promise in protecting complex systems and represent a step towards self-management of complexity