Autonomic Computing (AC) is a type of computing that comprises attributes that allow for self-management. This technology has been started by IBM. This conceptually advanced computing enables itself to perform adaptive decisions and constant up-grading using optimization and adaptation.
The increasing demand for computers means more computer-related problems. This demands skilled workers. This causes the need for autonomic computers that can do computing operations with human help.
The Autonomic Cognitive Architectures team at IBM are constantly working on improving Artificial Intelligence (AI) models so they are more adaptive while also being optimized according to their environment.
The recent advances in computing have made systems more complex and difficult to deal with. To overcome this problem, Autonomic Computer (AC) systems can be designed which will help regulate complexity on an individual level by adapting themselves without human intervention.
Autonomic Computing (AC) Architecture
Control loops are provided, which are embedded in the runtime environment. A manageability interface is used to configure every component such as a hard drive.
The managed element can be hardware or software, which is a component of the controlled system. It is controlled by sensors and effectors.
The state, or changes to the state, of the elements of the automatic system are informed through the sensors.
To change the state of an element, commands or Application Programming Interfaces (API) are used.
The control loops are divided into four parts: monitor, analyze, plan, and execute. The control loop implementation is ensured using the autonomic manager.
Autonomic Computing Examples
Four areas of Autonomic Computing (AC) are defined by IBM:
With the changes in environment, the system must be able to configure itself
The system must be able to self-repair errors and route the functions away from those trouble areas
The system must be able to self-perform according to optimization and ensure that it follows an efficient algorithm for computer operations
The system must be able to protect itself from system attacks by detection and identification of those attacks so that the system’s security and integrity remain intact
Autonomic Computing Benefits
· It is an Open-Source solution
· It is adaptive to new changes
· It is optimized so that it performs more efficiently with less time for executions
· It is secure, being able to counter system attacks automatically
· It can recover from system failures and crashes with its backup mechanism
· The Total Cost of Ownership is reduced arising from less tendency for failures and self-maintenance
· It can self-setup, eliminating manual setup
Autonomic Computing Challenges
· There is always the possibility of a malfunctioning system or crash
· Autonomic Computing (AC) can contribute to increased unemployment after being implemented
· It is expensive so may not be affordable
· It will require highly skilled support employees, increasing labor costs
· Its performance is dependent on the quality of Internet speed
· It will not be set up in rural areas where there is no stable Internet service or connection