Autonomic Computing is a form of visionary computing that has been started by IBM. It has a function of continuous up-gradation using optimization and adaptation. It was suggested by Jiming Liu in the year 2001, and it uses artificial structures to solve difficult problems using imitations of those of humans. IBM Research Chief Paul Horn addressed an annual conference. He proposed a solution to growing complexities and stated that the response lay in constructing computer systems that could control themselves in a manner.
Autonomic computing is a computer’s ability to handle everything automatically by adaptive systems with further computing meaning and capacities to cut back on time taken by computer practitioners to overcome device problems and other maintenance such as software upgrades.
The move toward autonomic computation is motivated by a need for cost savings and the need to lift the barriers posed by computer system complexity to allow for more sophisticated computing technologies.
IBM has specified the four areas of automatic computing:
AC was programmed to replicate the human body’s nervous system-in that the autonomic nervous system behaves and responds to stimuli independent of the individual’s conscious input-an an autonomic computing environment that operates with a high degree of artificial intelligence while being invisible to the users. Just like the human body behaves and reacts without the person regulating processes, and the autonomic computing environment performs organically in response to the feedback it receives.
IBM has set out eight criteria that characterize an autonomic system:
Autonomic computing is like the building blocks of big data and an anticipated future computing paradigm in which small – perhaps invisible – machines will be everywhere over us, interacting across rapidly interconnected networks contributing to the idea of the Internet of Everything. Many business leaders are studying different components of autonomic computation with autonomic computing examples. There are advantages and disadvantages of autonomic computing too. Let us discuss the benefits of it :
The key advantage of autonomic computing is decreased TCO (Total Cost of Ownership). Breakdowns would be less common with significantly lowering maintenance costs. A very few staffs will be required to operate the networks. “The benefit of autonomic computing will be reduced in maintenance cost, deployment, time and increased stability of IT systems through automation”.
Another advantage of this technology is that it provides data consolidation to optimize system capacity and minimizes expense and human activity to maintain massive server farms.
With the increase in the market for computers, computer-related issues are also growing. They are getting more and more dynamic. The uncertainty has become so much that there is an increase in demand for professional staff. This has fostered the need for autonomic machines that can do computational operations without the need for human interference.
Autonomic computing architecture promises to automate the management of computing devices. But the capability would provide the basis for even more efficient Cloud Computing. Applications of autonomic computing include server load balancing, process allocation, memory error-correction, controlling power supply, automatic upgrading of software and drivers, pre-failure notification, automated device backup and recovery, etc.
Autonomic computation is a framework that deploys high-level policies to make decisions. It is based on the architecture that is called MAPE, which means tracking, evaluation of the plan, and execution. The design focuses on the concept of a decrease in administration costs. The above article has explained the concept of autonomic computing along with its advantages and disadvantages of autonomic computing.
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