發(fā)布時間：2018-07-28 13:27

【摘要】：任何軟件系統(tǒng)都不可避免地發(fā)生軟件衰退,虛擬化系統(tǒng)自然也不例外,因此有必要研究適合于虛擬化系統(tǒng)的自愈技術。虛擬化系統(tǒng)通常采用虛擬機在線遷移技術實現(xiàn)負載均衡、故障緩解等管理功能以提高系統(tǒng)可用性,而軟件自愈技術作為一種預防性維護和管理技術,是降低系統(tǒng)衰退導致的系統(tǒng)失效風險、提高系統(tǒng)可用性的更為主動的方法。因此,本文融合了軟件自愈技術和虛擬機在線遷移技術,以軟件自愈分析模型作為自愈技術研究的核心內(nèi)容,研究基于虛擬機在線遷移的自愈分析模型和虛擬機在線遷移優(yōu)化,分析自愈策略的成本,試圖為實施自愈技術提供決策依據(jù)。本文完成的主要工作和取得的研究成果如下:(1)針對單服務器虛擬化系統(tǒng)的軟件衰退問題,采用形式化模型描述了應對衰退的自愈策略,以虛擬機監(jiān)視器和/或虛擬機集群為自愈對象分別構造了四種不同的自愈策略對應的自愈分析模型,采用了馬爾可夫再生理論進行模型分析,并給出了一種最優(yōu)自愈策略的搜索方法。仿真實驗表明合理的自愈策略不僅能有效提升系統(tǒng)可用性,而且能降低系統(tǒng)停機成本。(2)針對多服務器虛擬化系統(tǒng)中虛擬機監(jiān)視器和虛擬機之間的強依賴關系,利用隨機回報網(wǎng)模型分析了基于時間虛擬機冷自愈模型、虛擬機熱自愈模型和虛擬機遷移自愈模型,并提出了一個融入負載因素的基于時間和負載的虛擬機遷移自愈模型。仿真實驗結(jié)果表明無論穩(wěn)態(tài)可用性還是事務丟失數(shù)量,虛擬機遷移自愈策略均優(yōu)于虛擬機冷/熱自愈策略;融入負載因素的自愈模型無論在系統(tǒng)可用性和吞吐率等指標方面都優(yōu)于基于時間策略的自愈模型,且在系統(tǒng)負載動態(tài)變化時表現(xiàn)地更為穩(wěn)定。(3)為了降低多服務器虛擬化系統(tǒng)中基于虛擬機遷移的自愈成本,提出了一個虛擬機動態(tài)遷移預拷貝優(yōu)化算法MSTO。該算法根據(jù)時間局部性和空間局部性原理,設計了一種動態(tài)的遷移窗口設定和遷移頁面選擇策略,以適應于動態(tài)的頁面臟度和遷移鏈路可用帶寬。實驗結(jié)果表明MSTO在停機時間、總遷移時間和遷移總頁數(shù)等方面均優(yōu)于Xen的預拷貝方法。
[Abstract]:Any software system inevitably occurs software decline, virtualization system is no exception, so it is necessary to study self-healing technology suitable for virtualization system. Virtual machine online migration technology is usually used to achieve load balancing, fault mitigation and other management functions to improve system availability, while software self-healing technology as a preventive maintenance and management technology, It is a more active way to reduce the risk of system failure and improve system availability. Therefore, this paper combines the software self-healing technology and virtual machine online migration technology, taking the software self-healing analysis model as the core of the self-healing technology research, studies the self-healing analysis model based on virtual machine online migration and the virtual machine on-line migration optimization. This paper analyzes the cost of self-healing strategy and tries to provide decision basis for implementing self-healing technology. The main work and research results of this paper are as follows: (1) aiming at the problem of software degradation in single-server virtualization system, a formal model is used to describe the self-healing strategy. Taking virtual machine monitor and / or virtual machine cluster as self-healing objects, four self-healing analysis models corresponding to different self-healing strategies are constructed, and Markov reproducing theory is used to analyze the model. A search method of optimal self-healing strategy is given. Simulation results show that reasonable self-healing strategy can not only effectively improve system availability but also reduce system downtime cost. (2) aiming at the strong dependence between virtual machine monitor and virtual machine in multi-server virtualization system. The cold self-healing model, the thermal self-healing model and the migration self-healing model of virtual machine based on time and load are analyzed by using stochastic return net model, and a model of virtual machine migration self-healing based on time and load is proposed. Simulation results show that virtual machine migration self-healing strategy is superior to virtual machine cold / hot self-healing strategy regardless of steady-state availability or transaction loss. The self-healing model incorporating load factors is superior to the time-based self-healing model in terms of system availability and throughput. In order to reduce the self-healing cost of virtual machine migration in multi-server virtualization system, a dynamic migration pre-copy optimization algorithm MSTO is proposed. According to the principle of time locality and spatial locality, this algorithm designs a dynamic migration window setting and migration page selection strategy to adapt to the dynamic page dirt and the available bandwidth of the migration link. The experimental results show that MSTO is superior to Xen in terms of downtime, total migration time and total number of pages.
【學位授予單位】：南京理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP302

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本文編號：2150298