面向云數(shù)據(jù)中心的虛擬機(jī)調(diào)度機(jī)制研究
發(fā)布時間：2018-08-22 19:14
【摘要】：近幾年,云計算蓬勃發(fā)展并得到了產(chǎn)業(yè)界和學(xué)術(shù)界的廣泛關(guān)注,已經(jīng)成為信息化建設(shè)領(lǐng)域的熱點和未來趨勢。虛擬化技術(shù)是實現(xiàn)云計算的關(guān)鍵技術(shù)之一,也是云數(shù)據(jù)中心資源管理的主要手段。由于不同的虛擬機(jī)與物理機(jī)之間的映射關(guān)系,會有不同的資源效率,使得虛擬機(jī)調(diào)度問題是云數(shù)據(jù)中心資源管理的一個重要問題�，F(xiàn)有研究大多集中在利用虛擬機(jī)調(diào)度方案來減少能耗、提高應(yīng)用性能、提供容錯處理等方面。但是,很少有人研究虛擬機(jī)在不同物理機(jī)上的調(diào)度對數(shù)據(jù)中心網(wǎng)絡(luò)的影響,而網(wǎng)絡(luò)作為數(shù)據(jù)中心應(yīng)用的共享部件,直接影響到應(yīng)用的性能。本文側(cè)重于把數(shù)據(jù)中心網(wǎng)絡(luò)因素納入虛擬機(jī)調(diào)度研究,設(shè)計不同的虛擬機(jī)調(diào)度機(jī)制,主要涉及能耗優(yōu)化、網(wǎng)絡(luò)性能優(yōu)化兩方面。從云提供者的角度出發(fā),重點考慮如何制定合理的虛擬資源調(diào)度策略,在保障租客資源需求和網(wǎng)絡(luò)性能的前提下,降低能源消耗,保證云數(shù)據(jù)中心高效地資源管理。 本文首先系統(tǒng)總結(jié)了虛擬化的相關(guān)研究背景,按照技術(shù)特征,把當(dāng)前面向云數(shù)據(jù)中心的虛擬機(jī)調(diào)度機(jī)制分成靜態(tài)放置和動態(tài)遷移兩類。然后,從這兩方面對虛擬機(jī)調(diào)度機(jī)制做了比較全面的歸納和總結(jié)。接著,以減少數(shù)據(jù)中心能耗、優(yōu)化網(wǎng)絡(luò)性能等問題作為切入點,結(jié)合云數(shù)據(jù)中心網(wǎng)絡(luò)拓?fù)渑c流量的特征,深入研究虛擬機(jī)調(diào)度機(jī)制,以優(yōu)化資源效率,從而提升數(shù)據(jù)中心網(wǎng)絡(luò)性能和減小運營成本。主要研究內(nèi)容包括服務(wù)器與網(wǎng)絡(luò)相結(jié)合能耗優(yōu)化的虛擬機(jī)放置方法、利用虛擬機(jī)放置改善網(wǎng)絡(luò)流量分布、能耗-網(wǎng)絡(luò)性能均衡優(yōu)化的虛擬機(jī)調(diào)度機(jī)制、數(shù)據(jù)中心網(wǎng)絡(luò)擁塞感知的虛擬機(jī)遷移算法等四個方面,具體內(nèi)容如下： (一)研究了服務(wù)器與網(wǎng)絡(luò)相結(jié)合能耗優(yōu)化的虛擬機(jī)放置方法。在IaaS云中,虛擬機(jī)如何放置在物理機(jī)上,才能降低能耗,這是云提供者關(guān)心的一個重要問題。已有的虛擬機(jī)放置問題解決方案,有些是優(yōu)化物理服務(wù)器能耗,有些是優(yōu)化網(wǎng)絡(luò)設(shè)備能耗,較少人研究兩者的結(jié)合。本研究提出了一種同時優(yōu)化物理服務(wù)器和網(wǎng)絡(luò)設(shè)備(如交換機(jī)、路由器、網(wǎng)絡(luò)鏈路等)能耗的虛擬機(jī)放置方案,關(guān)閉／休眠空閑服務(wù)器、交換機(jī)、網(wǎng)絡(luò)鏈路等物理資源的數(shù)量,以降低能耗。把這種虛擬機(jī)放置問題抽象為基于物理機(jī)大小和鏈路容量相結(jié)合約束的裝箱問題,這是經(jīng)典的組合優(yōu)化問題,也是NP難問題。設(shè)計了一種改進(jìn)的蟻群優(yōu)化算法來求解。仿真實驗表明,與只優(yōu)化主機(jī)或只優(yōu)化網(wǎng)絡(luò)設(shè)備能耗算法相比,減小了數(shù)據(jù)中心能耗。 (二)研究了利用虛擬機(jī)放置技術(shù)優(yōu)化數(shù)據(jù)中心網(wǎng)絡(luò)流量分布。在云數(shù)據(jù)中心中,隨著虛擬化技術(shù)的廣泛應(yīng)用,不同的虛擬機(jī)放置對數(shù)據(jù)中心網(wǎng)絡(luò)流量有何影響是重要的研究點。已有的虛擬機(jī)放置問題解決方案,聚焦在服務(wù)器資源優(yōu)化,較少考慮當(dāng)前數(shù)據(jù)中心網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)和虛擬機(jī)之間的流量。本研究提出了一種虛擬機(jī)放置方案,同時優(yōu)化數(shù)據(jù)中心網(wǎng)絡(luò)總流量和鏈路利用率,從而使得數(shù)據(jù)中心網(wǎng)絡(luò)流量分布均衡,避免擁塞鏈路的產(chǎn)生。把虛擬機(jī)放置對網(wǎng)絡(luò)流量的優(yōu)化模型抽象為二次分配問題,這是經(jīng)典的組合優(yōu)化問題,也是NP難問題。采用蟻群優(yōu)化與2-opt局部搜索相結(jié)合的算法來求解。仿真實驗表明,最大鏈路利用率下降20%,擁塞鏈路數(shù)目下降37%。 (三)研究了能耗-網(wǎng)絡(luò)性能均衡優(yōu)化的虛擬機(jī)調(diào)度方法。對于云提供者而言,如何設(shè)計虛擬機(jī)調(diào)度方案來減少能耗并能改善網(wǎng)絡(luò)性能是一個重要問題�，F(xiàn)在的虛擬機(jī)調(diào)度研究也大多集中在物理服務(wù)器能耗或網(wǎng)絡(luò)設(shè)備能耗的優(yōu)化,然而隨著這些資源的聚合,有可能會帶來網(wǎng)絡(luò)性能的下降。本研究提出了一種虛擬機(jī)調(diào)度方案,給出了兩階段的調(diào)度策略：(1)虛擬機(jī)靜態(tài)放置,抽象為多維條件約束的裝箱問題和二次分配問題的結(jié)合,減少云數(shù)據(jù)中心激活物理服務(wù)器、交換機(jī)、網(wǎng)絡(luò)鏈路等物理資源的數(shù)量,從而減少能耗。(2)虛擬機(jī)動態(tài)遷移,在最小化遷移代價的前提下,最小化最大鏈路利用率來改善網(wǎng)絡(luò)性能。此方案的目的在于優(yōu)化網(wǎng)絡(luò)性能的同時,來減少服務(wù)器和網(wǎng)絡(luò)設(shè)備的能耗。為此設(shè)計了一種二階段的啟發(fā)式算法來求解,較好地實現(xiàn)了能耗與網(wǎng)絡(luò)性能的均衡優(yōu)化。 (四)研究了數(shù)據(jù)中心網(wǎng)絡(luò)擁塞感知的虛擬機(jī)遷移算法。在當(dāng)前云數(shù)據(jù)中心中,網(wǎng)絡(luò)會頻繁發(fā)生擁塞,導(dǎo)致丟包增多、時延增大和吞吐量下降,網(wǎng)絡(luò)已成為數(shù)據(jù)中心的性能瓶頸,數(shù)據(jù)中心網(wǎng)絡(luò)擁塞問題是當(dāng)前亟待解決的一個重要問題。傳統(tǒng)的解決網(wǎng)絡(luò)擁塞的方法,大多采用流路徑重新調(diào)度、修改網(wǎng)絡(luò)協(xié)議棧等。本研究嘗試采用虛擬機(jī)遷移技術(shù)來改變流量發(fā)送端或接受端在網(wǎng)絡(luò)拓?fù)渲械奈恢?使得流量避開擁塞鏈路,來減少網(wǎng)絡(luò)擁塞的發(fā)生,優(yōu)化網(wǎng)絡(luò)流量布局。與修改流路徑或協(xié)議棧相比較,這種方法很好地利用云計算彈性服務(wù)的特征,更為簡單和直接。在遷移時,主要考慮兩方面的代價：(1)最小化網(wǎng)絡(luò)總流量代價；(2)最小化遷移代價。提出了一種基于貪婪策略的三階段虛擬機(jī)遷移算法,算法時間復(fù)雜度較低。仿真實驗表明此算法在滿足遷移代價閾值的前提下,減小了數(shù)據(jù)中心網(wǎng)絡(luò)擁塞數(shù)。
[Abstract]:In recent years, cloud computing has developed vigorously and attracted extensive attention from industry and academia, and has become a hot spot and future trend in the field of information construction. Virtualization technology is one of the key technologies to achieve cloud computing, and is also the main means of resource management in cloud data center. Nowadays, most of the researches focus on using virtual machine scheduling schemes to reduce energy consumption, improve application performance, and provide fault-tolerant processing. However, few people study the scheduling of virtual machines on different physical machines. The impact of central network, as a shared component of data center applications, directly affects the performance of applications. This paper focuses on the data center network factors into the virtual machine scheduling research, design different virtual machine scheduling mechanisms, mainly involving energy consumption optimization, network performance optimization two aspects. From the perspective of cloud providers, the focus is on Consider how to formulate a reasonable virtual resource scheduling strategy to reduce energy consumption and ensure efficient resource management of cloud data center under the premise of guaranteeing tenant resource demand and network performance.
Firstly, the research background of virtualization is summarized systematically. According to the technical characteristics, the virtual machine scheduling mechanism for cloud data center is divided into static placement and dynamic migration. Network performance and other issues as a starting point, combined with the characteristics of cloud data center network topology and traffic, in-depth study of the virtual machine scheduling mechanism to optimize resource efficiency, thereby improving data center network performance and reducing operating costs. Virtual machine scheduling mechanism for improving network traffic distribution, energy consumption-network performance balancing and optimization, and virtual machine migration algorithm for network congestion awareness in data center can be described as follows:
(1) The method of virtual machine placement for energy consumption optimization of server and network is studied. In the IaaS cloud, how to place virtual machines on physical machines to reduce energy consumption is an important concern of cloud providers. This study proposes a virtual machine placement scheme that simultaneously optimizes the energy consumption of physical servers and network devices (such as switches, routers, network links, etc.), shuts down/hibernates idle servers, switches, network links and other physical resources to reduce energy consumption. The problem is abstracted as a packing problem with constraints of physical machine size and link capacity. It is a classical combinatorial optimization problem and NP-hard problem. An improved ant colony optimization algorithm is designed to solve the problem.
(2) The optimization of network traffic distribution in data centers using virtual machine placement technology is studied. With the wide application of virtualization technology in cloud data centers, the impact of different virtual machine placements on network traffic in data centers is an important research point. In this paper, a virtual machine placement scheme is proposed to optimize the total network traffic and link utilization of the data center, so that the network traffic distribution of the data center is balanced and the congestion link is avoided. Type I is abstracted as quadratic assignment problem, which is a classical combinatorial optimization problem and NP-hard problem. Ant colony optimization combined with 2-opt local search algorithm is used to solve this problem. Simulation results show that the maximum link utilization rate decreases by 20% and the number of congested links decreases by 37%.
(3) Virtual machine scheduling method for energy-network performance balancing optimization is studied. For cloud providers, how to design a virtual machine scheduling scheme to reduce energy consumption and improve network performance is an important issue. In this paper, a virtual machine scheduling scheme is proposed and two-stage scheduling strategy is proposed: (1) static placement of virtual machines, abstracted as a combination of multi-dimensional constrained packing problem and secondary allocation problem, which reduces the activation of physical servers and switches in cloud data centers. (2) Virtual machine dynamic migration can improve network performance by minimizing the maximum link utilization while minimizing the migration cost. The purpose of this scheme is to optimize network performance and reduce the energy consumption of servers and network devices. Heuristic algorithm is used to solve the problem and achieve better energy consumption and network performance optimization.
(4) This paper studies the virtual machine migration algorithm for network congestion awareness in data centers. In current cloud data centers, network congestion occurs frequently, resulting in increased packet loss, increased latency and throughput degradation. Network has become the performance bottleneck of data centers. In this study, we try to use virtual machine migration technology to change the location of traffic sender or receiver in the network topology, so that traffic avoids congested links, to reduce network congestion and optimize network traffic layout. Compared with path or protocol stack, this method makes good use of the characteristics of cloud computing flexible services, which is simpler and more direct. When migrating, we mainly consider two costs: (1) minimizing the total network traffic cost; (2) minimizing the migration cost. The simulation results show that the algorithm can reduce the number of network congestion in data center while satisfying the threshold of migration cost.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP393.01
本文編號：2198047
[Abstract]:In recent years, cloud computing has developed vigorously and attracted extensive attention from industry and academia, and has become a hot spot and future trend in the field of information construction. Virtualization technology is one of the key technologies to achieve cloud computing, and is also the main means of resource management in cloud data center. Nowadays, most of the researches focus on using virtual machine scheduling schemes to reduce energy consumption, improve application performance, and provide fault-tolerant processing. However, few people study the scheduling of virtual machines on different physical machines. The impact of central network, as a shared component of data center applications, directly affects the performance of applications. This paper focuses on the data center network factors into the virtual machine scheduling research, design different virtual machine scheduling mechanisms, mainly involving energy consumption optimization, network performance optimization two aspects. From the perspective of cloud providers, the focus is on Consider how to formulate a reasonable virtual resource scheduling strategy to reduce energy consumption and ensure efficient resource management of cloud data center under the premise of guaranteeing tenant resource demand and network performance.
Firstly, the research background of virtualization is summarized systematically. According to the technical characteristics, the virtual machine scheduling mechanism for cloud data center is divided into static placement and dynamic migration. Network performance and other issues as a starting point, combined with the characteristics of cloud data center network topology and traffic, in-depth study of the virtual machine scheduling mechanism to optimize resource efficiency, thereby improving data center network performance and reducing operating costs. Virtual machine scheduling mechanism for improving network traffic distribution, energy consumption-network performance balancing and optimization, and virtual machine migration algorithm for network congestion awareness in data center can be described as follows:
(1) The method of virtual machine placement for energy consumption optimization of server and network is studied. In the IaaS cloud, how to place virtual machines on physical machines to reduce energy consumption is an important concern of cloud providers. This study proposes a virtual machine placement scheme that simultaneously optimizes the energy consumption of physical servers and network devices (such as switches, routers, network links, etc.), shuts down/hibernates idle servers, switches, network links and other physical resources to reduce energy consumption. The problem is abstracted as a packing problem with constraints of physical machine size and link capacity. It is a classical combinatorial optimization problem and NP-hard problem. An improved ant colony optimization algorithm is designed to solve the problem.
(2) The optimization of network traffic distribution in data centers using virtual machine placement technology is studied. With the wide application of virtualization technology in cloud data centers, the impact of different virtual machine placements on network traffic in data centers is an important research point. In this paper, a virtual machine placement scheme is proposed to optimize the total network traffic and link utilization of the data center, so that the network traffic distribution of the data center is balanced and the congestion link is avoided. Type I is abstracted as quadratic assignment problem, which is a classical combinatorial optimization problem and NP-hard problem. Ant colony optimization combined with 2-opt local search algorithm is used to solve this problem. Simulation results show that the maximum link utilization rate decreases by 20% and the number of congested links decreases by 37%.
(3) Virtual machine scheduling method for energy-network performance balancing optimization is studied. For cloud providers, how to design a virtual machine scheduling scheme to reduce energy consumption and improve network performance is an important issue. In this paper, a virtual machine scheduling scheme is proposed and two-stage scheduling strategy is proposed: (1) static placement of virtual machines, abstracted as a combination of multi-dimensional constrained packing problem and secondary allocation problem, which reduces the activation of physical servers and switches in cloud data centers. (2) Virtual machine dynamic migration can improve network performance by minimizing the maximum link utilization while minimizing the migration cost. The purpose of this scheme is to optimize network performance and reduce the energy consumption of servers and network devices. Heuristic algorithm is used to solve the problem and achieve better energy consumption and network performance optimization.
(4) This paper studies the virtual machine migration algorithm for network congestion awareness in data centers. In current cloud data centers, network congestion occurs frequently, resulting in increased packet loss, increased latency and throughput degradation. Network has become the performance bottleneck of data centers. In this study, we try to use virtual machine migration technology to change the location of traffic sender or receiver in the network topology, so that traffic avoids congested links, to reduce network congestion and optimize network traffic layout. Compared with path or protocol stack, this method makes good use of the characteristics of cloud computing flexible services, which is simpler and more direct. When migrating, we mainly consider two costs: (1) minimizing the total network traffic cost; (2) minimizing the migration cost. The simulation results show that the algorithm can reduce the number of network congestion in data center while satisfying the threshold of migration cost.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP393.01
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 張彬彬;羅英偉;汪小林;王振林;孫逸峰;陳昊罡;許卓群;李曉明;;虛擬機(jī)全系統(tǒng)在線遷移[J];電子學(xué)報;2009年04期
，本文編號：2198047
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