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首页> 外文会议>International Conference on Advances in Computing, Communications and Informatics >Dynamic Load Balancing Strategy based on Resource Classification Technique in IaaS Cloud
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Dynamic Load Balancing Strategy based on Resource Classification Technique in IaaS Cloud

机译：IaaS云中基于资源分类技术的动态负载均衡策略

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摘要

Cloud computing is a utility-based model in the distributed environment which consists of various numbers of resources with heterogeneous servers. The diversity and increasing demands of the user applications lead to increasing resource demands, which makes the whole cloud data center as load imbalanced. The existing algorithms deal with the load distribution in a static and dynamic environment without dealing with a current load of the servers which may balance the load of the servers at certain time interval but not in the long run. So one of the biggest challenges in a cloud environment is to maximize the resource utilization of the servers and balance the load of the whole cloud data center for the long-term process. In order to meet the above-mentioned challenge, in this paper, we have devised a dynamic load balancing strategy based on a baseline neural network technique which will dynamically classify the servers based on the remaining load capacity of the server and deploy the task to the best-fit virtual machine instances on the optimal loaded server. This may minimize the total execution time of the tasks and maximize the resource utilization of the servers while balancing the load of the cloud data center for the long-term process. Finally, we compare the proposed approach over the existing strategies using various performance metrics.
机译：云计算是分布式环境中基于实用程序的模型，由各种数量的资源和异构服务器组成。用户应用程序的多样性和不断增长的需求导致资源需求的增长，这使得整个云数据中心的负载不平衡。现有算法在静态和动态环境中处理负载分布，而不处理服务器的当前负载，这可能会在一定时间间隔平衡服务器的负载，但从长远来看不会。因此，在云环境中，最大的挑战之一是在长期过程中最大化服务器的资源利用率并平衡整个云数据中心的负载。为了应对上述挑战，在本文中，我们设计了一种基于基线神经网络技术的动态负载平衡策略，该策略将根据服务器的剩余负载容量对服务器进行动态分类，并将任务部署到服务器上。最佳负载服务器上最适合的虚拟机实例。这可以最小化任务的总执行时间，并最大化服务器的资源利用率，同时平衡长期过程中云数据中心的负载。最后，我们使用各种性能指标将建议的方法与现有策略进行比较。

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