Grid resources are typically diverse in nature with respect to their software and hardware configurations, resource usage policies and the kind of application they support. Aggregating and monitoring these resources, and discovering suitable resources for the applications become a challenging issue. This is partially due to the representation of Grid metadata supported by the existing Grid middleware which offers limited scope for matching the job requirements that directly affect scheduling decisions. This paper proposes a semantic component in conventional Grid architecture to support ontology‐based representation of Grid metadata and facilitate context‐based information retrieval that complements Grid schedulers for effective resource management. Web Ontology language is used for creating Grid resource ontology and Algernon inference engine has been used for resource discovery. This semantic component has been integrated with conventional Grid schedulers. Several experiments have also been carried out to investigate the performance overhead that arises while integrating this component with Grid schedulers. Copyright © 2009 John Wiley & Sons, Ltd.

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Abstract-In a cloud computing, provisioned virtual machines are insufficient for computing the services it needs to be fine tuned by adding the additional resources or boosting the existing resources. A static method of resource allocation becomes inefficient under the different load. Based on the heavy load virtual machine can be adopted for dynamically changes of its behavior. There are two ways of scaling in the cloud scenario as horizontal scaling and vertical scaling. In a horizontal scaling, if the virtual machine resources are suffer from providing a service a new virtual machine was created and launched immediately. A threshold value has been maintained for scale out and scale down the virtual machine. Horizontal scaling is a traditional and well suitable approach for cloud computing environment. But the limitation of this approach is required separate load balancer to distribute the load between the virtual machines. In a vertical scaling resources are boosted by maximizing the virtual machine capabilities without shutting down the virtual machines. It's not like a traditional scaling approach to set the threshold and adding the resources. Here the resources are monitored by certain interval and based on the analysis resources are added to existing virtual machine. But lots of challenges are there in this approach. The limitation of this approach is we can't scale up the virtual machine up to the physical machine capability. Here we presented and analysis our approach for increasing the CPU capability of virtual machine. Likewise we can increase other resources like memory, vDisk and bandwidth. presented architecture resides on top of the virtual machine monitor and acts based on the scheduling algorithm.

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