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 improving its effectiveness. Self-protection: A self-protecting system is capable of de- fending itself from malicious attacks or cascading failures. Self-protection is potentially related to the decision making process, albeit the main issue is the attack detection mechanism. Self-healing: A self-healing system detects, diagnoses, and repairs localized problems resulting from bugs or failures in software and hardware. Self-optimization: A self-optimizing system is capable of monitoring and tuning itself according to performance analysis. Performance-based tuning strategies play a key role in the autonomic computing systems definition and are strictly related to the decision making process. An autonomic computing system is supposed to seek ways to improve its operation, identifying and seizing opportunities to be more efficient in performance or cost.
An administrative of Autonomic Computing (Chess)
“Systems manage themselves according to an administrator’s goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems” (Jeffrey O). One of the primary motivations behind autonomic computing (AC) is the problem of administrating highly complex systems. AC seeks to solve this problem through increased automation, relieving system administrators of many heavy activities. However, the AC strategy of managing complexity through automation runs the risk of making management harder. We performed field studies of current administrator work practices to inform the design of AC in order to ensure that it simplifies system management. In this paper, we analyze what system administrators do in terms of three important activities: Rehearsal and planning.
Maintaining situation awareness.
Managing multitasking, Interruptions and diversions.
System and network security are vital parts of any autonomic...
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