Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 4, April 2013, pg.623 – 627 RESEARCH ARTICLE © 2013, IJCSMC All Rights Reserved 623 A Novel Resource Distributed Discovery and Management in Grid Computing M. Brinda Kumar 1 , Dr. K.P. Kaliyamurthie 2 1 Department of Information Technology, Bharath University, India 2 Department of Information Technology, Bharath University, India Abstract— In grid-computing environment the computer resources are shared under the grid nodes. Resource discovery is an important process for finding suitable nodes that satisfy application requirements in grid environment. In most of the existing resource discovery mechanisms rely mainly on recent observed resource capacities of individual nodes to make their deployment decision based on current status of the nodes have severe limitations to achieve scalability because of the presence of internodes dynamism in addition to the internodes heterogeneity. Individual nodes have widely varying resource capabilities due to varying loads, network connectivity, churn, or user behavior. Besides internodes heterogeneity, many of these systems also show a high degree of internodes dynamism, so that selecting nodes based only on their recently observed resource capacities can lead to poor deployment decisions resulting in application failures or migration overheads. Key Terms: - Grid computing; Resource discovery I. INTRODUCTION Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach common goal. Hardware and software resources are shared within the grid nodes. It provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources such as computers, online instruments, storage space, data, and applications. Resources are identified based on a set of desired attributes. Resource attributes have various degrees of dynamism, from mostly static attributes, like operating system version, to highly dynamic ones, like network bandwidth or CPU load. Resource management systems use a system model to describe resources and a centralized scheduler to control their allocation. It does not adapt well to grid systems, to support high throughput computing. Obstacles include heterogeneity of resources, which make uniform allocation algorithms difficult to formulate, and distributed ownership, leading to widely varying allocation policies. By these problems, it developed and implemented the classified advertisement matchmaking framework, and general approach to resource management in grid environment with decentralized ownership of resources. Efficient resource discovery based on dynamic attributes such as CPU utilization and available bandwidth is a crucial problem in the deployment of computing grids. Existing solutions are either centralized or unable to answer advanced resource queries (e.g., range queries) efficiently. Aggregation particularly hierarchical aggregation is a common technique employed in large distributed systems for the scalable dissemination of information. Aggregation essentially compresses the amount of transmitted data in the system while preserving the overall information content. In the context of resource discovery, this would correspond to a suitable “compression” of the node resource usage patterns to achieve a desirable trade-off between the quality of resource discovery and the overhead of network data transmission in the system.