Overview of Autonomic Computing
Overview of Autonomic Computing
The concept of autonomic system is introduced in 2001by Dr. Paul Horn (Cybenko). This concept is a way to address the unsustainable growth in administration costs for model software, computing and networking system. There are some paradigm that this concept is introduces such as self-aware, self-repairing and self-optimizing application software, operating system and network infrastructure. Autonomic computing system has a sense of self-awareness which prevent IT administrator and manager to spend a lot of time to fixing configuration errors, tracking down hardware and software faults, restoring servers and application program because the sense of self-awareness of the autonomic computing system can track down a root cause for recovery, repair and diagnosis matter to take an appropriate action in order to return the system to the proper operating mode. for example the system can reboot or restart an application program or download the necessary updates to critical system code. Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal de- spite changing environmental conditions and demands. Autonomic systems manage themselves without human intervention, and their development involves a variety of exciting challenges. The “decide”, or equivalently the “analyze and plan”, phase is responsible for providing and enforcing the desired properties of the self- managing system. Thus, the design of the decision phase is essential for obtaining the desired self-configuring, self- healing, self-optimizing and self-protecting autonomic system. An autonomic computing system may be built with different goals, but its essence is self-management. Four main aspects of self-management emerge as follows: Self-configuration: A self-configuring system is able to configure itself according to high-level policies and objectives, thereby
The concept of autonomic system is introduced in 2001by Dr. Paul Horn (Cybenko). This concept is a way to address the unsustainable growth in administration costs for model software, computing and networking system. There are some paradigm that this concept is introduces such as self-aware, self-repairing and self-optimizing application software, operating system and network infrastructure. Autonomic computing system has a sense of self-awareness which prevent IT administrator and manager to spend a lot of time to fixing configuration errors, tracking down hardware and software faults, restoring servers and application program because the sense of self-awareness of the autonomic computing system can track down a root cause for recovery, repair and diagnosis matter to take an appropriate action in order to return the system to the proper operating mode. for example the system can reboot or restart an application program or download the necessary updates to critical system code. Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal de- spite changing environmental conditions and demands. Autonomic systems manage themselves without human intervention, and their development involves a variety of exciting challenges. The “decide”, or equivalently the “analyze and plan”, phase is responsible for providing and enforcing the desired properties of the self- managing system. Thus, the design of the decision phase is essential for obtaining the desired self-configuring, self- healing, self-optimizing and self-protecting autonomic system. An autonomic computing system may be built with different goals, but its essence is self-management. Four main aspects of self-management emerge as follows: Self-configuration: A self-configuring system is able to configure itself according to high-level policies and objectives, thereby
References: Bindelli, S. (n.d.). Building Autonomic Components: the SelfLets Approach. Chess, D. M. (n.d.). Security in an autonomic computing evironment . Cybenko, G. (n.d.). Practical Autonomic Computing. Ionescu, D. (n.d.). A Robust Autonomic Computing Architecture for Server Virtualization. Jeffrey O, D. M. (n.d.). the vision of autonomic computing . Khalid, A. (2009). Survey of Frameworks, Architectures and Techniques in Autonomic Computing.