A Novel QoS-Aware Load Balancing Mechanism in Cloud Environment

Feng Ye, Shengyan Wu, Qian Huang, Xu An Wang
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引用次数: 1

Abstract

Efficient low-level resource provisioning and QoS guaranteed are key challenges for cloud computing. When using virtual machines cluster to tackle various tasks scheduling, the target is to assign the tasks to each of the available nodes evenly in in premise of ensuring QoS of services, and it also means that the cloud providers should consider to reduce the load of overload nodes, and improve resource utilization of under-loading nodes. There are some limitations when applying these classic scheduling algorithms to the cloud computing environment. In order to solve this problem, we propose a novel QoS-aware load balancing mechanism in cloud environment. The key of this mechanism includes QoS model, resource model, and task model. We conduct a CloudSim based experiment to evaluate our method using a realistic dataset, and the results show that the algorithm proposed effectively shortens the waiting time in comparison to RR algorithm and Max-Min algorithm.
云环境下一种新的qos感知负载均衡机制
高效的底层资源配置和QoS保证是云计算面临的主要挑战。在使用虚拟机集群处理各种任务调度时,目标是在保证服务QoS的前提下,将任务均匀地分配到每个可用的节点上,这也意味着云提供商应该考虑降低过载节点的负载,提高负载不足节点的资源利用率。在将这些经典调度算法应用于云计算环境时存在一些限制。为了解决这一问题,我们提出了一种新的云环境下qos感知负载均衡机制。该机制的关键包括QoS模型、资源模型和任务模型。我们利用一个真实的数据集进行了基于CloudSim的实验来评估我们的方法,结果表明,与RR算法和Max-Min算法相比,我们提出的算法有效地缩短了等待时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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