Autonomic Computing

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Autonomic Computing is an initiative started by IBM in 2001. Its ultimate aim is to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth. In other words, autonomic computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes whilst hiding intrinsic complexity to operators and users. An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by Autonomic Components (AC) interacting with each other [1]. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planer/adapter for exploiting policies based on self- and environment awareness.

Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed.A very similar trend has recently characterized significant research work in the area of multi-agent systems. However, most of these approaches are typically conceived with centralized or cluster-based server architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services.

Autonomy-oriented computation is a paradigm proposed by Jiming Liu, in 2001 that uses artificial systems imitating social animals' collective behaviours to solve hard computational problems. For example, ant colony optimization could be studied in this paradigm.[1]

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[edit] The problem of growing complexity

Self-management means different things in different fields: The number of computing devices in use is forecast to grow at 38% per annum and the average complexity of each is increasing [2]. Currently this volume and complexity is managed by highly skilled humans; but the demand for skilled IT personnel is already outstripping supply, with labour costs exceeding equipment costs by a ratio of up to 18:1 [2]. Computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance.

In ‘The Vision of Autonomic Computing’[3], Kephart and Chess warn that the dream of interconnectivity of computing systems and devices could become the “nightmare of pervasive computing” in which architects are unable to anticipate, design and maintain the complexity of interactions. They state the essence of autonomic computing is system self-management, freeing administrators from low-level task management while delivering more optimal system behavior.

A general problem of modern distributed computing systems is that their complexity, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer networks for communication and computation. The distributed applications running on these computer networks are diverse and deal with many different tasks, ranging from internal control processes to presenting web content and to customer support.

Additionally, Mobile computing is pervading these networks at an increasing speed: employees need to communicate with their companies while they are not in their office. They do so by using laptops, PDAs, or mobile phones with diverse forms of wireless technologies to access their companies' data.

This creates an enormous complexity in the overall computer network which is hard to control manually by one or more human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer system tends to increase very quickly.

80% of such problems in infrastructure happen at the client specific application and database layer. Most 'autonomic' service providers guarantee only up to the basic plumbing layer (power, hardware, operating system, network and basic database parameters).

[edit] Autonomic systems

A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems. It is inspired by the autonomic nervous system of the human body. This nervous system controls important bodily functions (e.g. respiration, heart rate, and blood pressure) without any conscious intervention.

In a self-managing Autonomic System, the human operator takes on a new role: He does not control the system directly. Instead, he defines general policies and rules that serve as an input for the self-management process. For this process, IBM has defined the following four functional areas:

  • Self-Configuration: Automatic configuration of components;
  • Self-Healing: Automatic discovery, and correction of faults;
  • Self-Optimization: Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements;
  • Self-Protection: Proactive identification and protection from arbitrary attacks.

IBM defined five evolutionary levels, or the Autonomic deployment model, for its deployment: Level 1 is the basic level that presents the current situation where systems are essentially managed manually. Levels 2 - 4 introduce increasingly automated management functions, while level 5 represents the ultimate goal of autonomic, self-managing systems.

[edit] Control loops

A basic concept that shall be applied in Autonomic Systems are closed control loops. This well-known concept stems from Process Control Theory. Essentially, a closed control loop in a self-managing system monitors some resource (software or hardware component) and autonomously tries to keep its parameters within a desired range.

According to IBM, hundreds or even thousands of these control loops are expected to work in a large-scale self-managing computer system.

[edit] See also

[edit] References

  1. ^ Xiaolong Jin and Jiming Liu, "From Individual Based Modeling to Autonomy Oriented Computation", in Matthias Nickles, Michael Rovatsos, and Gerhard Weiss (editors), Agents and Computational Autonomy: Potential, Risks, and Solutions, pages 151–169, Lecture Notes in Computer Science, vol. 2969, Springer, Berlin, 2004. ISBN 978-3-540-22477-8.
  2. ^ ‘Trends in technology’, survey, Berkeley University of California, USA, March 2002
  3. ^ IEEE Computer Magazine, Jan 2003

[edit] External links

Retrieved from "http://en.wikipedia.org/wiki/Autonomic_Computing"
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