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Distributed Dynamic load balancing (DDLB) is an important system function destined to distribute workload among available processors to improve throughput and/or execution times of parallel computer in Cluster Computing. Instead of balancing the load in cluster by process migration, or by moving an entire process to a less loaded computer, we make an attempt to balance load by splitting processes into separate jobs and then balance them to nodes. In order to get target, we use mobile agent (MA) to distribute load among nodes in a cluster. In this study, a multi-agent framework for load balancing in heterogeneous cluster is given. Total load on node is calculated using queue length which is measured as the total number of processes in queue. We introduce types of agents along with policies needed to meet the requirements of the proposed load-balancing. Different metrics are used to compare load balancing mechanism with the existing message passing technology. The experiment is carried out on cluster of PC's divided into multiple LAN's using PMADE (Platform for Mobile agent distribution and execution). Preliminary experimental results demonstrated that the proposed framework is effective than the existing ones.

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This paper presents an enhanced approach to recognize objects based on a similarity measure obtained from shape context. Typically, shape context computation samples at regular interval on the contour of an object without regard to landmarks. Corner points of an object being landmarks on the contour; set of corner points is a good descriptor of shape. The paper explores the possibility of computing shape context by sampling the corner points using arch height. Sampling on this boundary feature of the object considerably reduces the number of points. Landmark based shape context provides a stricter algorithm on similarity. Shape context based object recognition being an iterative process involving comparisons and transformations, the reduction in the number of sample points provides a basis for faster recognition of similar objects.

No abstract