The new era of autonomics

The way computers manage themselves is being redefined. Here’s why.

Back in 2001, IBM launched its model of autonomic computing. It was based on the human body’s autonomic nervous system, which regulates unconscious bodily functions. The type of things that you do without thinking about it, such as breathing and digestion.

What’s changed is the advance of Artificial Intelligence (AI) and cloud computing. Autonomic computing was traditionally about the “body” of a system. However, AI is about the “head” – thinking and making intelligent decisions. And cloud is the enabler – the oxygen and blood supply. Combine these three innovations, and the potential for a fully developed self-sufficient system is clear.

Looking back

Back at the turn of the decade, IBM’s vision was for IT infrastructure to act in the same way. Managing complex processes automatically, without the need for human involvement. If there was a malfunction, an engineer would become involved. Playing the “doctor” role and “healing” the system.

IBM outlined four areas of autonomics: configuration, healing (repairing), protection (guarding against security threats) and optimisation (balancing resources). These would incorporate eight conditions that defined autonomics:

  1. know itself in terms of what resources it has access to, what its capabilities and limitations are and how and why it is connected to other systems
  2. be able to automatically configure and reconfigure itself depending on the changing computing environment
  3. be able to optimise its performance to ensure the most efficient computing process
  4. be able to work around encountered problems by either repairing itself or routing functions away from the trouble
  5. detect, identify and protect itself against various types of attacks to maintain overall system security and integrity
  6. The system must be able to adapt to its environment as it changes, interacting with neighbouring systems and establishing communication protocols
  7. rely on open standards and cannot exist in a proprietary environment
  8. anticipate the demand on its resources while keeping transparent to users

The rise of autonomics was welcomed by companies offering outsourced customer services, such as callcentres. Delivering automated call-answering was one way to compete with countries where operating costs are lower.

Looking forward

But that wasn’t all. There was another driver for autonomics, which offers exciting possibilities when combined with AI. If computers manage infrastructure, this removes barriers to making the infrastructure more complex. If humans don’t need to learn how every element works, computers can take control, and develop new ways of working.

Why is this particularly relevant now? The explosion in cloud computing, coupled with developments in AI, means that the advantages of autonomics can be realised in a wider range of industries. Scalable, agile, and capable of handling high volumes of data… cloud makes this possible, by offering a hybrid option. Ideal for balancing legacy architecture with autonomic innovations.

Of course, there’s still a way to go before “virtual engineers” are seen as a de facto solution for businesses. The advantages of big data have been well-documented. But building the infrastructure to crunch it? And finding analysts with the necessary level of expertise? That’s more of a challenge.

However, now the “head” of AI can lead the “body” of autonomics, with a cloud-powered circulatory system. Computers will evolve and “learn” at a rate far faster than humans, who will be there to guide, rather than program. This is a new wave of autonomics. The future is going to arrive faster than ever before.

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