A review on architecture and models for autonomic software systems

Abstract

Autonomic computing was the term coined by IBM in 2001. The term autonomic computing was used to define the self-adaptable nature of the human body. According to IBM, the same self-adaptable feature was the need to be incorporated in the software systems. Autonomic computing is the combination of few self-capabilities such as self-configuration, self-healing, self-optimization, self-protection, self-awareness, etc. So, autonomic computing approach was then used to develop autonomic software systems. This approach makes the computing systems self-adaptable and self-decision-making support systems for various activities. It also helps to reduce the human intervention in the software management process. Though, the implementation of autonomic self-capabilities may increase the software complexity, which further requires human intervention for the software maintenance-related specific tasks. Still, IT industries are approaching to develop autonomic features in their existing architecture or developing new self-adaptable software systems. Autonomic computing has its importance for providing a bridge for handling and managing the run-time computation-based issues/exceptions of the software. So, the discussion of this solution has become a necessity for making the vision of autonomic decision making more clear and understandable for researchers and developers for the improvement in an autonomic area. The paper provides an insight vision of the autonomic decision-making concept and its importance for the various purposes such as intrusion detection, cloud-based data security, wireless sensor network, Internet of Things, Big Data and many other areas where management cannot be handled by a human in real time. To assess the degree of autonomic feature, there is another term used which is known as autonomicity. The paper also discusses some solutions suggested and implemented by different researchers during their studies for estimating the system’s autonomicity level. These solutions will help in comparing different autonomic applications based on the autonomic features implemented in each application. This paper is an attempt to provide better understandability in the autonomic computational field.