Swarm Intelligence in Autonomic Computing: The Particle Swarm Optimization Case

Introduction

One of the scientific areas of autonomic computing is the investigation of mechanisms that exploit the collaborative behavior of the agents in order to deal with the information discovery. Specifically, in discovering contextual information an agent (e.g., mobile node) needs to explore, locate and track the source that generates the required contextual information – context (e.g., environmental parameters like temperature, humidity, situations like fire outbreak) for the executing context-aware, mobile application (e.g., the control of a group of robots). On the other hand, Swarm Intelligence (SI) (including Ant Colony Optimization, Particle Swarm Optimization, and Stochastic Diffusion Search) introduces a powerful new paradigm for building fully distributed systems in which overall system functionality is attained by the interaction of individual agents with each other and with their environment. Such agents coordinate using decentralized control and self-organization. Swarm systems are intrinsically highly parallel and exhibit high levels of robustness and reliability.

In this chapter, we discuss the application of Particle Swarm Intelligence in autonomic computing and networking. We map the basic concepts and definitions of particle swarm intelligence to autonomic computing and investigate the contextual information exploration (detection, discovery and exploitation) in autonomous dynamic environments. Specifically we focus on those scenarios where autonomous nodes with autonomic and context aware applications are autonomously adjusting their knowledge, policies and learning models in trying to (even physically) locate up-to-date contextual information, captured by other nodes. We also establish the concept of contextual information quality (an ageing framework deprecates contextual information thus leading to low quality) and how the nodes autonomously attempt to discover context in a collaborative manner. Nodes with low quality context cannot capture such information by themselves but are in need for “fresh” context in order to feed their application. In addition, we discuss the introduction of particle swarm optimization process in contextual information discovery and we report the performance of the reported algorithms in this area through simulations. Finally, we report a set of exercises for experimenting on the discussed algorithms focusing on better understanding of the swarm intelligence framework in autonomic computing.