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Unequal-interval based loosely coupled control method for auto-scaling heterogeneous cloud resources for web applications

机译:基于不平衡的网络应用程序自动缩放异构云资源的不平等耦合控制方法

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摘要

Most existing quality of service (QoS) control algorithms of Web applications take into account Web Server or database connections which can be released immediately. However, many applications are deployed on virtual machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval-priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to overcontrol. Fluctuated prices make Spot VMs unreliable due to unexpected termination which makes fault-tolerant strategies crucial. In this article, an unequal-interval-based loosely coupled control method is proposed to improve the quality of service (QoS) control ability of fault-tolerant strategies. A queuing model with arrival-rate-adjustment coefficient is used to predict required capacity as a feedforward controller. Another two-threshold and queuing-model-based method is applied to update the coefficient as a loosely coupled feedback controller. Meanwhile, unequal-interval controller collaborating method is proposed to avoid overcontrol and react quickly to workload changes. Our approach is evaluated on both a simulation platform and a real Kubernetes Cluster. Experimental results illustrate that our approach decreases the percentage of waiting times larger than service level agreements with similar or lower rental costs compared with existing algorithms.
机译:最现有的Web应用程序的服务质量(QoS)控制算法考虑了可以立即释放的Web服务器或数据库连接。但是,许多应用程序部署在虚拟机(VMS)上,甚至从公共云弹性租用的斑点VM。为了节省成本,在租用间隔的目的之前,不会释放间隔的VM。这种控制效应的延迟使现有的方法租用或释放超越VM,导致过度控制。由于意外终止,波动的价格使得现场VMS不可靠,这使得容错策略至关重要。在本文中,提出了一种不平等的间隔的松散耦合控制方法,以提高容错策略的服务质量(QoS)控制能力。具有到达速率调整系数的排队模型用于预测所需的馈电控制器。应用另一种双阈值和基于模型的方法来将系数更新为松散耦合的反馈控制器。同时,提出了不平等间隔的控制器协作方法,以避免过度控制并快速反应工作负载变化。我们的方法是在仿真平台和真正的Kubernetes集群上进行评估。实验结果表明,与现有算法相比,我们的方法降低了与具有相似或更低租金成本的服务水平协议的等待时间。

著录项

  • 来源
    《Concurrency, practice and experience》 |2020年第23期|e5926.1-e5926.16|共16页
  • 作者单位

    Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China|Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Parkville Vic Australia;

    Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China;

    Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China;

    Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Parkville Vic Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cloud computing; feedback control; queuing model; resource provisioning; Spot VM;

    机译:云计算;反馈控制;排队模型;资源供应;点VM;

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