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Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management

机译:基于神经网络的动态VM整合算法的云资源自适应选择

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摘要

Cloud resource management becomes more important with the increasing usage of cloud resources. With various cloud options available, cloud provider may have different priority in managing the resource through resource scheduling and provisioning. Dynamic VM (Virtual Machine) consolidation algorithm is one of the techniques which can be used to reduce energy consumption through VM migration. Higher VM migration may lead to lower energy consumption and higher SLA violation. Although previous research has successfully decreased energy consumption and SLA violation, cloud providers may need to manage trade-offs between energy and SLA violation through availability of priority in the system. This paper proposes neural network-based adaptive selection of VM consolidation algorithms which adaptively chooses appropriate algorithm according to cloud provider's goal priority and environment parameters. Dataset generation and performance evaluation using simulations on real-world PlanetLab VMs workload trace showed that adaptive selector produced better average performance score than independent methods on various evaluation priority. (C) 2018 Elsevier B.V. All rights reserved.
机译:随着云资源使用的增加,云资源管理变得越来越重要。利用各种可用的云选项,云提供商可以通过资源调度和供应来在管理资源方面具有不同的优先级。动态VM(虚拟机)整合算法是可用于通过VM迁移减少能耗的技术之一。较高的VM迁移可能导致较低的能耗和较高的SLA违规。尽管先前的研究成功地减少了能耗和违反SLA的行为,但是云提供商可能需要通过系统优先级的可用性来管理能耗和违反SLA的折衷。提出了一种基于神经网络的VM合并算法自适应选择方法,根据云提供商的目标优先级和环境参数自适应选择合适的算法。使用真实PlanetLab VM工作负载跟踪上的仿真进行的数据集生成和性能评估表明,在各种评估优先级上,自适应选择器产生的平均性能得分要高于独立方法。 (C)2018 Elsevier B.V.保留所有权利。

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