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ECLB: A Novel Exhaustive Criterion Based Load Balancing Algorithm for E-Learning Platform by Data Grid Technologies

机译:ECLB:基于数据网格技术的基于穷举准则的新型学习平台负载均衡算法

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Virtual learning is a method to access and share study materials for students on web. As it is the easy way of accessingdata, millions of student’s widely using E-learning. Due to the increase of users day by day there is an issue inscheduling and providing service to plenty of users, thus load balancing among the nodes is a great deal faced by server.This makes many research people to carry out their work in load sharing and balancing between nodes. Though theexisting methods provide solution for load sharing and balancing it requires high cost. In this paper, we mainly focus onallocating loads among nodes in a network with minimum cost. K-means algorithm is used to cluster the node.Clustering increases the performance of load balancing by forming tighter clusters. To share load, among interclusteringand for intra clustering we use the proposed method, Exhaustive Criterion based Load Balancing (ECLB)algorithm. The experimental results show that the load is properly and effectively shared among nodes with minimumcost.
机译:虚拟学习是一种在网上访问和共享学生学习材料的方法。由于它是访问数据的简便方法,因此数以百万计的学生正在广泛使用电子学习。由于用户的日益增加,为大量用户安排时间和提供服务存在问题,因此节点之间的负载平衡是服务器面临的一大难题。这使得许多研究人员在负载共享和共享方面进行工作。节点之间的平衡。尽管现有方法提供了用于负载共享和平衡的解决方案,但是它需要高成本。在本文中,我们主要集中在以最小成本在网络中的节点之间分配负载。 K-means算法用于对节点进行群集。群集通过形成更紧密的群集来提高负载平衡的性能。为了在集群间和集群内集群之间共享负载,我们使用提出的基于穷举准则的负载平衡(ECLB)算法。实验结果表明,负载可以在节点之间以最小的成本正确有效地分担。

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