Abstract
Modern networks offer end-to-end connectivity however; the increasing amount of traditional offered services may still not fulfill the requirements of ever demanding distributed applications and must therefore be enriched by some form of increased intelligence in the network. This is where the promise of autonomous systems comes into play. Paul Horn of IBM Research first suggested the idea of autonomic computing on 15 October 2001 at the Agenda conference in Arizona. The need centers around the exponential growth of networking complexity. Autonomous systems are capable of performing activities by taking into account the local environment and adapting to it. No planning is required hence autonomous systems simply have to make the best of the resources at hand. Locality in this scenario is no longer geographical but rather the information and applications on the boundary of the autonomic communicating element which may be distributed over a wide area. The most common definition of an autonomic computing system is one which can control the functioning of computer applications and systems without input from the user, in the same way that the autonomic nervous system regulates body systems without conscious input from the individual. Thus, we attempt here to more clearly identify the need for autonomous systems, their architecture, the path of evolution from traditional network elements and the future of such systems.
Key Terms in this Chapter
Autonomic: Relating to, or controlled by the autonomic nervous system.
Self Healing: Having the power or property of healing one’s self or itself. Autonomic computing refers to the ability of systems to self-diagnose and self-heal without the need for operator intervention.
Distributed Computing: A system where tasks are divided among multiple computers rather than having all processes originating from one main central computer. Client/server systems are one type of distributed computing. It can also be described as a system in which services are provided by teams of computers collaborating over a network.
Grid Computing: A computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure. GRID Computing is basically taking a number of inexpensive personal computers and connecting them via a network to build a supercomputer, which can utilize the idle processing time on each machine to carry out tasks that would have previously required an expensive mainframe. One comparison that is often used to describe a computational GRID is that of the electrical GRIDs responsible for providing electricity.
Self-Management: The process by which computer systems manage their own operation without human intervention.
Pervasive Computing: This is the trend toward increasingly ubiquitous connected computing devices in the environment and particularly, wireless technologies and the Internet. Pervasive computing devices are not broadly speaking personal computers as we tend to think of them, but rather small (often micro like)—electronic mobile embedded devices in almost any type of real world object, including cars, tools, household appliances, clothes, and so forth—all communicating through increasingly interconnected networks.
Closed Control Loop: This technique stems from process control theory. A closed control loop in a self-managing system monitors some resource and autonomously tries to keep its parameters within a desired range.