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Computer Science
Distributed Systems

Autonomic Computing: An Overview

Profile image of Salim HaririSalim Hariri

2005, Lecture Notes in Computer Science

https://doi.org/10.1007/11527800_20
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Abstract

The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challengesa vision that has been referred to as autonomic computing. The overarching goal of autonomic computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of autonomic computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to autonomic computing, its challenges, and opportunities.

Figures (6)
Fig. 1. Essential variables Ashby observed that many organisms undergo two forms of disturbances: (1) fre- quent small impulses to the main variables and (2) occasional step changes to its param- eters. Based on this observation, he devised the architecture of the Ultra-Stable system that consists of two closed loops (see Figure 2): one that controls small disturbances and a second that is responsible for longer disturbances.
Fig. 1. Essential variables Ashby observed that many organisms undergo two forms of disturbances: (1) fre- quent small impulses to the main variables and (2) occasional step changes to its param- eters. Based on this observation, he devised the architecture of the Ultra-Stable system that consists of two closed loops (see Figure 2): one that controls small disturbances and a second that is responsible for longer disturbances.
Fig. 3. Nervous system as part of an ultrastable system
Fig. 3. Nervous system as part of an ultrastable system
Fig. 4. An autonomic element
Fig. 4. An autonomic element
Table 1. Systems incorporating autonomic properties projects and products can be broadly classified as (1) systems that incorporate auto- nomic mechanisms for problem determination, monitoring, analysis, management, etc., into systems, and (2) systems that investigate models, programming paradigms and de- velopment environments to support the development of autonomic systems and applica- tions. A sampling of systems belonging to these categories are summarized in Tables | and Table 2 respectively.
Table 1. Systems incorporating autonomic properties projects and products can be broadly classified as (1) systems that incorporate auto- nomic mechanisms for problem determination, monitoring, analysis, management, etc., into systems, and (2) systems that investigate models, programming paradigms and de- velopment environments to support the development of autonomic systems and applica- tions. A sampling of systems belonging to these categories are summarized in Tables | and Table 2 respectively.
Table 2. Systems supporting development of autonomic applications and systems monitor, analyze, plan and execute using knowledge of the environment. Several re- search efforts focused on enabling the autonomic properties address four main ar- eas: self-healing, self-protection, self-configuration, and self-optimization. Projects in both industry and academia, have addressed autonomic behaviors at all levels, from the hardware level to software systems and applications. At the hardware level, sys- tems may be dynamically upgradable, while at the operating system level, active op- erating system code may be replaced dynamically. Efforts have also focused on auto- nomic middleware, programming systems and runtime. At the application level, self- optimizing databases and web servers dynamically reconfigure to adapt service per- formance. These efforts have demonstrated both the feasibility and promise of auto- nomic computing. However, achieving overall autonomic behaviors remains an open and significant challenge, which will be accomplished through a combination of pro- cess changes, skills evolution, new technologies and architecture, and open industry standards.
Table 2. Systems supporting development of autonomic applications and systems monitor, analyze, plan and execute using knowledge of the environment. Several re- search efforts focused on enabling the autonomic properties address four main ar- eas: self-healing, self-protection, self-configuration, and self-optimization. Projects in both industry and academia, have addressed autonomic behaviors at all levels, from the hardware level to software systems and applications. At the hardware level, sys- tems may be dynamically upgradable, while at the operating system level, active op- erating system code may be replaced dynamically. Efforts have also focused on auto- nomic middleware, programming systems and runtime. At the application level, self- optimizing databases and web servers dynamically reconfigure to adapt service per- formance. These efforts have demonstrated both the feasibility and promise of auto- nomic computing. However, achieving overall autonomic behaviors remains an open and significant challenge, which will be accomplished through a combination of pro- cess changes, skills evolution, new technologies and architecture, and open industry standards.

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