Decision Making and Autonomic Computing
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 15, Issue 1
Abstract
Autonomic Computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by autonomic components interacting with each other. An Autonomic Computing can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user
Authors and Affiliations
Rajyagor Bhargav P.
Keywords
Related Articles
- EP ID EP152051
- DOI -
- Views 104
- Downloads 0
Penetrating Windows 8 with syringe utility
: Windows 8, the most popular operating system by Microsoft launched in October 2012. It is developed for use of desktops, laptops, tablets, home theatre PC’s. Windows 8 is more secure than previous version...
Analysis of Rayleigh Quotient in Extrapolation Method to Accelerate the Computation Speed of PageRank
The development of techniques for computing PageRank efficiently for Web-scale graphs is very important since computing a PageRank vector of Web graphs containing a billion nodes can take several days. Previous m...
DBET: Demand Based Energy efficient Topology for MANETs
Abstract: Energy efficient topology in Ad-hoc networks can be achieved mainly in two different ways. In the first method, network maintains a small number of nodes to form a connected backbone and the remaining nod...
Review paper on adapting data stream mining concept drift using ensemble classifier approach
Abstract: Data stream is massive, fast changing and infinite in nature. It is very natural that large amount of unlabeled data and small amount labeled are available in data stream environments. Storing and labelin...
Segmentation of Tumor Region from Brain Mri Images Using Fuzzy C-Means Clustering And Seeded Region Growing
Abstract: The detection of brain tumor is one of the most challenging tasks in the field of medical image processing, since brain images are very complicated and tumors can be analyzed efficiently only by the expert radi...