Тарқатылған есептеу ресурстарының өзін-өзі басқару қасиеттері

Self managing characteristics of distributed computing resources
Autonomic computing (AC) is distributed computing resources with self managing characteristics, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by IBM in 2001, this initiative ultimately aimed 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.

The AC system concept is designed to make adaptive decisions, using high level policies. It will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An AC can be modeled in terms of two main control schemes (local and global) with sensors (for self monitoring), effectors (for self adjustment), knowledge and planner/adapter for exploiting policies based on self and environment awareness. This architecture is sometimes referred to as Monitor Analyze Plan Execute (MAPE). 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 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. Some autonomic systems involve mobile agents interacting via loosely coupled communication mechanisms. Autonomy oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve difficult computational problems. For example, ant colony optimization could be studied in this paradigm.

The design complexity of Autonomic Systems can be simplified by utilizing design patterns such as the model–view–controller (MVC) pattern to improve concern separation by encapsulating functional concerns.

A basic concept that will 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.

A fundamental building block of an autonomic system is the sensing capability (Sensors Si), which enables the system to observe its external operational context. Inherent to an autonomic system is the knowledge of the Purpose (intention) and the Know how to operate itself (e. g., bootstrapping, configuration knowledge, interpretation of sensory data, etc.) without external intervention. The actual operation of the autonomic system is dictated by the Logic, which is responsible for making the right decisions to serve its Purpose, and influence by the observation of the operational context (based on the sensor input). This model highlights the fact that the operation of an autonomic system is purpose driven. This includes its mission (e. g., the service it is supposed to offer), the policies (e. g., that define the basic behaviour), and the "survival instinct". If seen as a control system this would be encoded as a feedback error function or in a heuristically assisted system as an algorithm combined with set of heuristics bounding its operational space.