本文選題：云計(jì)算 + OpenStack�。� 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：云計(jì)算技術(shù)的發(fā)展已經(jīng)改變了傳統(tǒng)IT架構(gòu),它給傳統(tǒng)物理資源的管理和利用帶來(lái)了革命性的變化。隨著云計(jì)算技術(shù)的廣泛使用,如何讓服務(wù)器集群中的資源得到充分均衡的利用已成為資源調(diào)度研究的熱點(diǎn)問(wèn)題之一。通過(guò)改進(jìn)虛擬機(jī)資源調(diào)度策略來(lái)解決上述負(fù)載不均衡度問(wèn)題,同時(shí)還能夠改善資源利用率和減少能耗。本文主要基于OpenStack云平臺(tái)的虛擬機(jī)資源調(diào)度策略進(jìn)行研究。首先,本文研究了基于網(wǎng)絡(luò)帶寬感知的虛擬機(jī)調(diào)度策略。當(dāng)前主流的OpenStack開(kāi)源云平臺(tái)虛擬機(jī)調(diào)度機(jī)制是基于CPU、內(nèi)存和存儲(chǔ)資源來(lái)進(jìn)行部署的,而沒(méi)有考慮對(duì)網(wǎng)絡(luò)帶寬資源的合理調(diào)度,因此,本文考慮了網(wǎng)絡(luò)帶寬資源的約束問(wèn)題,完善了OpenStack云平臺(tái)針對(duì)網(wǎng)絡(luò)帶寬需求的虛擬機(jī)調(diào)度策略,均衡了網(wǎng)絡(luò)帶寬資源的利用。仿真結(jié)果表明,本文提出的調(diào)度策略與OpenStack默認(rèn)的調(diào)度策略相比,更有效的提升了虛擬機(jī)實(shí)例的網(wǎng)絡(luò)吞吐量。其次,針對(duì)OpenStack云平臺(tái)中多目標(biāo)約束問(wèn)題,提出了基于粒子群和蟻群的多目標(biāo)融合算法的虛擬機(jī)調(diào)度策略,該策略能夠在多個(gè)相互矛盾的目標(biāo)中尋求折中。多目標(biāo)融合算法是對(duì)粒子群和蟻群算法的改進(jìn),本文提出了關(guān)于物理集群的資源浪費(fèi)、能源損耗和負(fù)載不均衡度模型,通過(guò)算法融合來(lái)加速迭代搜索虛擬機(jī)映射到物理主機(jī)的Parato最優(yōu)解,仿真表明,本文提出的多目標(biāo)融合算法與傳統(tǒng)的元啟發(fā)式算法粒子群算法和蟻群算法相比,能夠改善多維資源利用率、降低能耗和負(fù)載不均衡度。最后,本文設(shè)計(jì)并實(shí)現(xiàn)了基于OpenStack的虛擬機(jī)調(diào)度平臺(tái)。主要對(duì)監(jiān)控模塊和數(shù)據(jù)庫(kù)模塊進(jìn)行了設(shè)計(jì)。該平臺(tái)能夠?qū)μ摂M機(jī)和物理主機(jī)資源使用情況進(jìn)行監(jiān)測(cè),通過(guò)虛擬機(jī)調(diào)度算法來(lái)優(yōu)化下一次的位置部署,從而提升多維資源的利用率和均衡負(fù)載。
[Abstract]:The development of cloud computing technology has changed the traditional IT architecture, which has brought revolutionary changes to the management and utilization of traditional physical resources. With the wide use of cloud computing technology, how to make full and balanced use of resources in server clusters has become one of the hot issues in resource scheduling research. The problem of load imbalance can be solved by improving the resource scheduling strategy of virtual machine. At the same time, it can also improve resource utilization and reduce energy consumption. This paper mainly studies the virtual machine resource scheduling strategy based on OpenStack cloud platform. Firstly, this paper studies the virtual machine scheduling strategy based on network bandwidth awareness. The current mainstream OpenStack open source cloud platform virtual machine scheduling mechanism is based on CPU, memory and storage resources to deploy, without considering the reasonable scheduling of network bandwidth resources, therefore, this paper considers the constraints of network bandwidth resources. The virtual machine scheduling strategy of OpenStack cloud platform is improved to meet the demand of network bandwidth, and the utilization of network bandwidth resources is balanced. Simulation results show that compared with OpenStack's default scheduling strategy, the proposed scheduling strategy improves the network throughput of virtual machine instances more effectively. Secondly, aiming at the multi-objective constraint problem in OpenStack cloud platform, a virtual machine scheduling strategy based on particle swarm and ant colony fusion algorithm is proposed. Multi-objective fusion algorithm is an improvement on particle swarm optimization and ant colony algorithm. In this paper, a model of resource waste, energy loss and load imbalance for physical cluster is proposed. The algorithm fusion is used to accelerate the iterative search virtual machine mapping to the Parato optimal solution of the physical host. The simulation results show that the proposed multi-objective fusion algorithm is compared with the traditional meta-heuristic algorithm particle swarm optimization algorithm and ant colony algorithm. It can improve multi-dimensional resource utilization and reduce energy consumption and load imbalance. Finally, this paper designs and implements a virtual machine scheduling platform based on OpenStack. The monitoring module and database module are designed. The platform can monitor the use of virtual machine and physical host resources, optimize the next location deployment by virtual machine scheduling algorithm, and improve the utilization and load balance of multi-dimensional resources.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09

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本文編號(hào)：2028116