Some application areas
Autonomic computing is expected to simplify the management of computing systems and open doors to applications such as seamless e-sourcing, grid computing and dynamic e-business.

E-sourcing is the ability to bring in IT as a utility, at the time it is required and in the needed quantity to complete the work.

Some other autonomic computing applications areas are memory error correction, server-load balancing, process allocation, monitoring power supply, automatic updating of software and drivers, automated system backup and recovery, and prefailure warning.

Autonomic cloud computing is related to empowering cloud infrastructures and platforms so that these can take their own decisions to incessantly achieve their assigned jobs. Cloud systems are required to consistently deliver their functionalities and facilities to users without any form of human intervention, interpretation and instruction.

Grid computing is one area where autonomic computing equipped with self-managing capabilities can add a lot of value, and there are many related projects in process.

University of Pennsylvania, the USA, is making a potent grid that targets to bring advanced methods of breast cancer diagnosis and screening to patients at a low cost. The grid is a utility-like service delivered over the Internet, enabling hundreds of hospitals to store mammograms in digital form. Analytical tools that aid doctors to diagnose individual cases and discover cancer clusters in the population are also available.

Then, there is North Carolina Biometrics Grid, which is available to thousands of researchers and educators to facilitate boosting the speed of genomic research that is likely to result in new medicines to fight diseases and grow more nutritious foods to satisfy global hunger.

Better access to higher computing power via grid computing integrated with the implementation of open standards will allow researchers to work together more easily on complex issues, which should benefit all mankind.

Fig. 7: Engineers are now beginning to understand that intuitive computing that calls for mimicking some or all aspects of the human brain (Image courtesy: http://programming4.us/enterprise/24086.aspx)

Weather forecasting and protein folding, where intricate medical calculations are needed, are application areas that need computers to work 24/7, continuously for a couple of years.

Progressively autonomic computers will provide tools to analyse these complex problems. Systems with mobile architecture, such as Blue Gene (Fig. 6), will allow the study of phenomena happening in split seconds at an atomic scale.

Autonomic computing will be able to better harness existing processing power to run complex mathematics for functions such as weather simulations and other scenarios that require public systems and infrastructure.

Human intervention will keep reducing in most tasks linked with systems management in the years to come. In fact, it will seem as pointless as asking a telephone operator for facilitating an STD call looks today.

Autonomic computing will make computers that serve you in some way, just like your airline, telecom operator, bank, and hotel, a reality. We are unlikely to hear responses such as “please try again later as our systems are slow or down.”

At the same time, autonomic features will begin to make way into client-level devices. This will allow the personal computer to finish several jobs, which till now required some level of human intervention, on its own.

Future scope
Perhaps, we have only discovered the tip of the autonomic computing iceberg and are oblivious of the many technical roadblocks that will come in the way. Autonomic computing is at an embryonic stage and there are several critical challenges to be overcome.

Some questions that need to be answered are:
How will we design our systems to define and redefine themselves in dynamic environments? (A system should know its periphery limits before it transacts with other systems.)

How will we build reliable interfaces and points-of-control while permitting a heterogeneous environment? (Multi-platforms create a multi-faceted situation for system administrators.)

How will we develop human interfaces that eliminate complexity and enable users to interact naturally with IT systems? (The final result needs to be crystal clear to the user.)

How can we bring together a group of autonomic components into a federated system? (Just creating autonomic components is insufficient.)

How can we design and support open standards that will perform? (Standardisation is critical as the era of proprietary solutions has ended.)

How can we produce adaptive algorithms to take past system knowledge and use those insights to perk up the rules? (Creative and new methods will be required to equip our systems to tackle the dynamic nature of environments and transactions.)

Research related to development of autonomic systems is indeed complex and challenging. However, future computer systems will need higher levels of automation if these are anticipated to manage the rapidly-increasing amounts of data, the ever-growing network and the rising force of processing power. While there are computers with various levels of automation, fully-autonomic systems remain a dream for the future.


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