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Autonomic computing

What is autonomic computing?

Autonomic computing refers to the self-managing characteristics of distributed computing resources, 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.

What are the 4 Areas Of Autonomic Computing?

There are four areas of Autonomic Computing as defined by IBM. These are as follows:

  1. Self-Configuration: The system must be able to configure itself automatically according to the changes in its environment.
  2. Self-Healing: IBM mentions that an autonomic system must have property by which it must be able to repair itself from errors and also route the functions away from trouble whenever they are encountered. 
  3. Self-Optimization: According to IBM an autonomic system must be able to perform in an optimized manner and ensure that it follows an efficient algorithm for all computing operations. 
  4. Self-Protection: IBM states that an autonomic system must be able to perform detection, identification, and protection from the security and system attacks so that systems’ security and integrity remain intact.

What's the autonomic computing architecture?

The AC architecture comprises attributes that allow self-management, according to various vendors by involving control loops. 

  1. Control loops: A resource provider provides control loops. It is embedded in the runtime environment. It is configured using a manageability interface that is provided for every resource e.g. hard drive.
  2. Managed Elements: The managed element is a component of the controlled system. It can be hardware as well as a software resource. Sensors and effectors are used to control the managed element.
  3. Sensors: This contains information about the state and any changes in the state of elements of the autonomic system.
  4. Effectors: These are commands or application programming interfaces (API) that are used to change the states of an element.
  5. Autonomic Manager: This is used to make sure that the control loops are implemented. This divides the loop into 4 parts for its functioning. These parts are monitor, analyze, plan, and execute.

 

The benefits of autonomic computing

The key advantage of autonomic computing is decreased Total Cost of Ownership. Breakdowns would be less common with significantly lowering maintenance costs. A very few staff members 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.

What are the goals of autonomic computing?

The main goal of autonomic computing is to realize computer and software systems that can manage themselves with little or no human interaction. An autonomic system is a system that can adapt to changes, which can be triggered by a change in the system’s own state (e.g., failure) or the state of its environment (e.g., external events). Accordingly, an autonomic system requires sensing mechanisms to sense changes (sensors), reaction mechanisms to respond to changes whwhen they happen (effectors), and a decision making engine (autonomic manager) to make the correct assessment. An autonomic system satisfies one or all autonomic self-∗ (read as self-star) properties, where self-∗ can be one of the following: self-configuring, self-healing, self-optimizing, and self-protecting.


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Autonomic computing

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is autonomic computing?

Autonomic computing refers to the self-managing characteristics of distributed computing resources, 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.

What are the 4 Areas Of Autonomic Computing?

There are four areas of Autonomic Computing as defined by IBM. These are as follows:

  1. Self-Configuration: The system must be able to configure itself automatically according to the changes in its environment.
  2. Self-Healing: IBM mentions that an autonomic system must have property by which it must be able to repair itself from errors and also route the functions away from trouble whenever they are encountered. 
  3. Self-Optimization: According to IBM an autonomic system must be able to perform in an optimized manner and ensure that it follows an efficient algorithm for all computing operations. 
  4. Self-Protection: IBM states that an autonomic system must be able to perform detection, identification, and protection from the security and system attacks so that systems’ security and integrity remain intact.

What's the autonomic computing architecture?

The AC architecture comprises attributes that allow self-management, according to various vendors by involving control loops. 

  1. Control loops: A resource provider provides control loops. It is embedded in the runtime environment. It is configured using a manageability interface that is provided for every resource e.g. hard drive.
  2. Managed Elements: The managed element is a component of the controlled system. It can be hardware as well as a software resource. Sensors and effectors are used to control the managed element.
  3. Sensors: This contains information about the state and any changes in the state of elements of the autonomic system.
  4. Effectors: These are commands or application programming interfaces (API) that are used to change the states of an element.
  5. Autonomic Manager: This is used to make sure that the control loops are implemented. This divides the loop into 4 parts for its functioning. These parts are monitor, analyze, plan, and execute.

 

The benefits of autonomic computing

The key advantage of autonomic computing is decreased Total Cost of Ownership. Breakdowns would be less common with significantly lowering maintenance costs. A very few staff members 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.

What are the goals of autonomic computing?

The main goal of autonomic computing is to realize computer and software systems that can manage themselves with little or no human interaction. An autonomic system is a system that can adapt to changes, which can be triggered by a change in the system’s own state (e.g., failure) or the state of its environment (e.g., external events). Accordingly, an autonomic system requires sensing mechanisms to sense changes (sensors), reaction mechanisms to respond to changes whwhen they happen (effectors), and a decision making engine (autonomic manager) to make the correct assessment. An autonomic system satisfies one or all autonomic self-∗ (read as self-star) properties, where self-∗ can be one of the following: self-configuring, self-healing, self-optimizing, and self-protecting.


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