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Autonomic computing is inspired by the human autonomic nervous system that handles complexity and uncertainties, andaims at realizing computing systems and applications capableof managing themselves with minimum human intervention. In this paper we first give an overview of the architecture of the autonomic nervous system and use it to motivate ourapproach to develop the autonomic computing paradigm. Wethen illustrate how this paradigm can be used to control andmanage complex applications. The human nervous system is, to the best of our knowledge,the most sophisticated example of autonomic behavior ex-isting in nature today [1]. It is the body’s master controllerthat monitors changes inside and outside the body integratessensory input, and effects appropriate response. In conjunc-tion with the endocrine system, which is the body’s secondimportant regulating system, the nervous system is able toconstantly regulate and maintain homeostasis. Homeostasisis one of the most remarkable properties of highly complexsystems. A homeostatic system (a large organization, an indus-trial firm, a cell) is an open system that maintains its structureand functions by means of a multiplicity of dynamic equilib-riums that are rigorously controlled by interdependent regu-lation mechanisms. Such a system reacts to every change inthe environment, or to every random disturbance, through aseries of modifications that are equal in size and opposite indirection to those that created the disturbance. The goal ofthese modifications is to maintain internal balances.