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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68923

    Title: 一個在 O p e n S t a c k 平台 進行 混 合 式 自 動 擴 展 的 方 法;A Hybrid Auto-Scaling Approach on OpenStack Cloud Platform
    Authors: 應帆;Ying,Fan
    Contributors: 資訊工程學系
    Keywords: 自動擴展;混合式;OpenStack;auto-scaling;hybrid
    Date: 2015-08-13
    Issue Date: 2015-09-23 14:46:40 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 虛擬化技術及網路環境尚未成熟前,機房建置主要透過實體機器組成伺服器群組來提供計
    如 OpenStack 的自動擴展機制,採用的方法是反應式自動擴展機制,也就是去檢查系統資
    來的工作負載,提早準備好資源因應。本論文並將統計學的預測方法實作到 OpenStack 上,
    關鍵字: 自動擴展、OpenStack、負載平衡、反應式自動擴展、預測式自動擴展、虛擬機。;The use of virtualization technology has gradually changed the way a datacenter works in recent
    years. Nowadays the end-users of a datacenter do not access physical resources directly. Instead,
    they access virtualized resources, such as VMs and virtual clusters, on top of a pool of physical
    resources. This new computing paradigm provides the datacenter administrators a more flexible,
    scalable, manageable, and economical way for resource provisioning/sharing as prior study
    indicated. When a service on a VM encounters a massive amount of workload, it can scale faster
    than a non-virtualized datacenter, by dynamically turning on extra virtual/physical machines to
    share the workload. For example, OpenStack, an open source project for building a virtualized
    cloud platform, provides a reactive approach for auto-scaling. That is, it creates new VMs to
    share workload when the workload of a monitored VM exceeds a given workload threshold. The
    weakness of the mechanism is that, sometimes it is too late to handle unexpected workload surges
    and thus can decrease the quality of the services running on the VM. To this end, we purpose a
    new hybrid auto-scaling mechanism for auto-scaling. It relies on a predictive auto-scaling
    approach that predicts the upcoming workload by historical workloads. To prevent the case that
    the prediction result is not accurate enough, we also use the reactive auto-scaling mechanism
    provided by OpenStack, and integrate the two mechanisms as one. We have verified the
    performance of our approach via experiments, and the results show that, when a massive
    workload arrives, the proposed approach outperforms other approaches. In addition, the proposed
    approach does not incur much overhead as the experimental results show.
    Keywords: Auto-scaling, reactive auto-scaling, predictive auto-scaling, load balancing, load
    sharing, virtual machine
    Appears in Collections:[資訊工程研究所] 博碩士論文

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