本文選題：云計(jì)算 + 虛擬化　； 參考：《山東大學(xué)》2017年碩士論文

【摘要】：目前在云計(jì)算蓬勃發(fā)展的同時(shí),也暴露了一系列問(wèn)題,最突出的問(wèn)題是集群的整體負(fù)載不均衡以及服務(wù)器的資源利用率低。首先數(shù)據(jù)中心規(guī)模逐步擴(kuò)大,針對(duì)不同業(yè)務(wù)的基礎(chǔ)設(shè)施標(biāo)準(zhǔn)也各不相同,數(shù)據(jù)中心設(shè)備的異構(gòu)性逐漸增強(qiáng),同時(shí)用戶的應(yīng)用需求數(shù)量激增,導(dǎo)致數(shù)據(jù)中心負(fù)載不均衡的現(xiàn)象普遍存在;其次傳統(tǒng)的資源調(diào)度并未考慮虛擬機(jī)是什么樣的應(yīng)用類型,而集群中不同應(yīng)用類型的虛擬機(jī)對(duì)應(yīng)不同的資源需求,這就導(dǎo)致同一宿主機(jī)的各種資源(網(wǎng)絡(luò)、計(jì)算或存儲(chǔ))的利用不均衡,造成資源浪費(fèi)。本文針對(duì)上述兩個(gè)問(wèn)題,提出一種基于KVM集群的負(fù)載均衡機(jī)制,研究工作主要包含四方面。首先設(shè)計(jì)了基于KVM集群的負(fù)載均衡機(jī)制的系統(tǒng),系統(tǒng)分為兩大部分——應(yīng)用端和管理端,應(yīng)用端為用戶提供各種應(yīng)用;管理端是整個(gè)系統(tǒng)的核心部分,包括服務(wù)器監(jiān)控模塊、虛擬機(jī)監(jiān)控模塊、負(fù)載評(píng)估模塊、彈性伸縮模塊、核心模塊以及彈性調(diào)整模塊。其中負(fù)載評(píng)估模塊根據(jù)本文設(shè)計(jì)的負(fù)載均衡算法求得可以衡量服務(wù)器負(fù)載壓力的權(quán)值,彈性伸縮模塊基于服務(wù)器的負(fù)載權(quán)值可以自動(dòng)調(diào)整KVM集群的規(guī)模,核心模塊由蟻群與模擬退火優(yōu)化算法決策出需要進(jìn)行調(diào)度的虛擬機(jī)的最佳匹配服務(wù)器,最后由彈性調(diào)整模塊實(shí)現(xiàn)虛擬機(jī)的遷移與系統(tǒng)的負(fù)載再評(píng)估。其次為了合理地衡量集群中物理機(jī)的負(fù)載壓力,本文提出一種負(fù)載均衡算法來(lái)負(fù)責(zé)監(jiān)控各個(gè)虛擬機(jī)的運(yùn)行狀態(tài)以及定期獲取各個(gè)應(yīng)用端的內(nèi)存利用率、CPU使用率、IO占用率和帶寬占用率,并結(jié)合服務(wù)器的硬件性能指標(biāo)以及當(dāng)前時(shí)刻服務(wù)器的運(yùn)行情況,求得數(shù)據(jù)中心中各個(gè)服務(wù)器的權(quán)值。同時(shí)根據(jù)權(quán)值實(shí)現(xiàn)系統(tǒng)的彈性伸縮、決定觸發(fā)遷移時(shí)機(jī)、確定需要進(jìn)行調(diào)度的虛擬機(jī)以及選擇合適的可遷移服務(wù)器,確保系統(tǒng)負(fù)載均衡,提高其可用性以及可靠性。之后本文提出了一種蟻群與模擬退火優(yōu)化算法,算法思想是通過(guò)用戶虛擬機(jī)和后臺(tái)宿主機(jī)之間的匹配度決策出需要進(jìn)行調(diào)度的虛擬機(jī)以及可遷移服務(wù)器的最佳匹配關(guān)系。然后利用彈性調(diào)整模塊實(shí)現(xiàn)虛擬機(jī)的遷移,并且在每次遷移完成后需對(duì)集群的負(fù)載壓力重新評(píng)估。最后通過(guò)CloudSim云平臺(tái)工具仿真測(cè)試基于本文提出的蟻群與模擬退火優(yōu)化算法的負(fù)載均衡機(jī)制,并與基于蟻群算法的負(fù)載均衡機(jī)制進(jìn)行對(duì)比,驗(yàn)證了本算法的可行性;同時(shí),在數(shù)據(jù)中心的KVM集群中運(yùn)行測(cè)試。通過(guò)實(shí)驗(yàn)表明,本文提出的基于KVM集群的負(fù)載均衡機(jī)制可以有效地提高集群每臺(tái)服務(wù)器的資源利用率,并且有效地改善了數(shù)據(jù)中心負(fù)載不均衡的現(xiàn)象。
[Abstract]:At present, cloud computing is booming, but also exposed a series of problems, the most prominent problem is the overall load of the cluster imbalance and low resource utilization of the server. First of all, the scale of the data center is gradually expanded, the infrastructure standards for different services are different, the heterogeneity of the data center equipment is gradually increasing, and the number of users' application demand is increasing rapidly. Secondly, the traditional resource scheduling does not consider what kind of application virtual machine is, and the virtual machines of different application types in the cluster correspond to different resource requirements. This leads to unbalanced utilization of all kinds of resources (network, computing or storage) in the same host, resulting in waste of resources. In this paper, a load balancing mechanism based on KVM cluster is proposed, which includes four aspects. First, the system of load balancing mechanism based on KVM cluster is designed. The system is divided into two parts: the application end and the management terminal, which provide various applications for the user, and the management terminal is the core part of the whole system, including the server monitoring module. Virtual machine monitoring module, load evaluation module, elastic expansion module, core module and elastic adjustment module. According to the load balancing algorithm designed in this paper, the load assessment module can get the weight value which can measure the load pressure of the server, and the elastic expansion module can automatically adjust the scale of the KVM cluster based on the load weight of the server. The core module is composed of ant colony and simulated annealing optimization algorithm to decide the optimal matching server of virtual machine which needs to be scheduled. Finally, the migration of virtual machine and the reevaluation of system load are realized by elastic adjustment module. Secondly, in order to reasonably measure the load pressure of the physical machines in the cluster, In this paper, a load balancing algorithm is proposed to monitor the running state of each virtual machine and to obtain the CPU utilization rate and the bandwidth utilization rate of each application side on a regular basis. The weights of each server in the data center are obtained by combining the hardware performance index of the server and the running situation of the server at the current time. At the same time, the flexibility of the system is realized according to the weights, the timing of triggering the migration is determined, the virtual machine that needs to be scheduled and the suitable portable server are selected to ensure the system load balance, improve the availability and reliability of the system. Then an ant colony and simulated annealing optimization algorithm is proposed. The algorithm is based on the matching degree between the user virtual machine and the background host to determine the optimal matching relationship between the virtual machine to be scheduled and the portable server. Then the virtual machine migration is realized by elastic adjustment module, and the load pressure of the cluster needs to be reevaluated after each migration. Finally, the load balancing mechanism of ant colony and simulated annealing optimization algorithm is tested by CloudSim cloud platform tool simulation, and compared with the load balancing mechanism based on ant colony algorithm, the feasibility of this algorithm is verified. Run the test in the KVM cluster in the data center. The experimental results show that the proposed load balancing mechanism based on KVM cluster can effectively improve the resource utilization of each server in the cluster and effectively improve the load imbalance phenomenon in the data center.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

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