Autonomic Computing was introduced by IBM to manage the complexity faced in the IT systems. As the fundament of the IT sector is heterogeneous, it requires competent influence. Autonomic computing brought a revolution to the IT industry by designing the computing systems which are self-managed. Autonomic computing has provided with high speed, maintenance and enabled essential functionaries without much human participation.
Four aspects of Autonomic computing are:
Self-healing environments can detect and interpret improper activity and immediately take corrective action. The rules of self-healing need to be defined before they are applied.
It is the ability to adapt to the changes in the system. The system must be able to configure, for example, install and update software automatically.
This is the ability to improve performance automatically. It overlooks and integrates the resources dynamically at runtime.
It is the ability to protect the information of the users and to protect data against any intrusions. It identifies valuable assets and protects it against numerous threats.
The main application of Autonomic computing is its self-healing systems. It increases the reliability and stability of the system by continuously monitoring and checking the components. It checks the self-awareness systems and detects any errors, and while it does that, the system returns to the previously used version and the defined algorithms to detect the point of the errors.
This technique aims to detect and repair any existing error and enable the system into a reconfiguring action without any assistance of a human being. IT industry was in dire need of a computer system that would make the professionals spend less time troubleshooting the problems and more on actual completion of the tasks.
It runs regression tests to modify its own behavior with a response to any changes in the environment. Self-healing systems maintain a satisfactory quality of the systems during runtime to avoid any improper conduct of the computer systems. That is the first phase of the cycle. The second phase of the cycle is to identify the error and diagnose it. The third phase is to plan a corrective action related to the error. Once the errors are troubleshooted, the cycle begins again.
The general idea of autonomy in the transport system is to manage the systems that are so complex that it’s a must that they are configured and optimized by themselves. Based on the idea of autonomic systems, it aligns the idea of transport studies and computer science that will give rise to a new system of the transport system.
The traffic flows will be optimized in urban areas with the necessity to solve the decision-making problems of the road operators. The basic structure of the control system comprises of the sensors of various types to implement control solutions by devices and systems.
The process is subject to human traffic manager who handles the traffic according to their understanding. These components are predicted to be included in the optimization process. The optimization also includes various sound inputs like weather conditions and the driver decisions which cannot be estimated.
Traffic control system follows the concept of bi-level formalism. It integrates the functionalities of the transportations systems. It manages the relative duration as well as the cycle of the signals. It decreases the optimization problems while increasing the solution space. The control processes target the optimization of transport behavior.
It also defines maximal and minimal values apart from the additional traffic characteristics. It keeps the network capacity and the nominal level whenever any sort of congestion appears in the network links. Autonomic computing minimizes the event of oversaturation and spills back the network links.
It is difficult to manage the complex systems of the IT sector. Virtualization is the technique of differentiating the resources of the system into various execution environments. With the help of virtualization, the data is safely migrated through virtual machines models.
It carries out major migrating activities while the operating systems run without any interruption, and this minimizes the service downtimes. Through virtualization, migration can also be conducted offline.
The autonomic computing models are differently remodeled to suit the current business structure. Some of its applications can be seen in Customer Relationship Management. The automated CRM manages the complexity that arises every day. The automated structure manages leads in a loop which is a self-optimizing component.
A simple algorithm manages and analyzes any errors relating to budget allocation, allocation of resources, revenue generated, and the execution of leads. Autonomic computing also manages pricing policies through various schemes that are available.
It also collects data through various modules and formulates automated inventory management. Parameters such as stock, market information, and the supplier information are checked.
With the additional systems and functionalities in the IT systems, the complexity is at the rise, which has led to more usage of Autonomic computing in various programs. It provides the system with correct information to the right user at the right time along with abundant resources as well to troubleshoot any problems.