本文選題：車聯(lián)網(wǎng) + 云計算��； 參考：《大連理工大學(xué)》2014年碩士論文

【摘要】：基于車聯(lián)網(wǎng)應(yīng)用的云計算支撐平臺利用虛擬化技術(shù)將不同類型的物理服務(wù)器和虛擬機(jī)等異構(gòu)資源整合成一個虛擬資源池,按需為不同的用戶提供不同類型的車聯(lián)網(wǎng)應(yīng)用服務(wù)。車聯(lián)網(wǎng)大部分具體應(yīng)用服務(wù)具有實時性強(qiáng)等特點,這些特點對車聯(lián)網(wǎng)云平臺提出了更高層次的要求。資源調(diào)度一直是云計算研究的重要課題之一,其調(diào)度問題的好壞直接影響到云平臺的穩(wěn)定性和服務(wù)的可靠性。因此,研究車聯(lián)網(wǎng)云平臺上的資源調(diào)度算法有著非常重要的理論和現(xiàn)實意義。 本文在車聯(lián)網(wǎng)應(yīng)用背景下,對云計算的資源調(diào)度問題進(jìn)行以下兩個方面的研究。 (1)車聯(lián)網(wǎng)應(yīng)用具有多用戶、多業(yè)務(wù)、高并發(fā)等特點。為了保障車聯(lián)網(wǎng)應(yīng)用在云平臺上快速、穩(wěn)定和可靠的運行,在云計算的基礎(chǔ)上,本文提出一種基于車聯(lián)網(wǎng)應(yīng)用的MCT-LB-GSA (Minimum Completion Time-Load Balance-Greedy Scheduling Algorithm)任務(wù)調(diào)度算法。算法以虛擬機(jī)資源的當(dāng)前負(fù)載作為約束條件,依照貪心策略將任務(wù)調(diào)度到當(dāng)前負(fù)載較輕且具有最小任務(wù)完成時間上的虛擬機(jī)資源上。在C1oudSim環(huán)境下進(jìn)行了實驗仿真,實驗結(jié)果證明：該算法在保證最優(yōu)任務(wù)調(diào)度跨度的同時也有效地實現(xiàn)了資源負(fù)載均衡,提高了資源利用率。 (2)車聯(lián)網(wǎng)應(yīng)用的特點與云計算數(shù)據(jù)中心物理主機(jī)配置的不一致通常會引起負(fù)載不均衡。針對該問題,本文提出一種基于車聯(lián)網(wǎng)的CDM-CU (Combining the distance matching and comprehensive utilization)虛擬機(jī)部署算法。本算法并不單純追求虛擬機(jī)和物理服務(wù)器性能向量的最優(yōu)距離,也不單純追求數(shù)據(jù)中心的最小負(fù)載,而是通過調(diào)和因子將二者靈活融合在一起,為用戶提交的業(yè)務(wù)選擇合適的物理主機(jī)來部署相應(yīng)的虛擬機(jī)集。在CloudS im環(huán)境下進(jìn)行了實驗仿真,實驗結(jié)果證明：該算法能在滿足個性化業(yè)務(wù)的基礎(chǔ)上取得很好的負(fù)載均衡。
[Abstract]:The cloud computing support platform based on vehicle networking applications integrates different types of heterogeneous resources such as physical servers and virtual machines into a virtual resource pool using virtualization technology and provides different types of vehicle networking application services to different users as needed. Most of the specific application services of vehicle networking have the characteristics of strong real-time, these characteristics put forward a higher level of requirements for the cloud platform of vehicle networking. Resource scheduling is one of the most important research topics in cloud computing. The quality of resource scheduling directly affects the stability of cloud platform and the reliability of service. Therefore, it is of great theoretical and practical significance to study the resource scheduling algorithm on the vehicle network cloud platform. In this paper, the following two aspects of resource scheduling of cloud computing are studied in the context of vehicle networking application. (1) vehicle networking applications have the characteristics of multi-user, multi-service, high concurrency and so on. In order to ensure the fast, stable and reliable operation of the vehicle networking application on the cloud platform, this paper presents a task scheduling algorithm based on cloud computing for MCT-LB-GSA / Minimum completion Time-Balance-Greedy scheduling algorithm. The algorithm takes the current load of the virtual machine resource as the constraint and schedules the task to the virtual machine resource with the lighter load and the minimum task completion time according to the greedy strategy. The simulation results under C1oudSim environment show that the proposed algorithm not only ensures the optimal task scheduling span but also realizes resource load balancing effectively. The characteristics of the vehicle network application are not consistent with the physical host configuration of the cloud computing data center, which usually lead to load imbalance. To solve this problem, a CDM-CU Combining the distance matching and comprehensive utilization) virtual machine deployment algorithm based on vehicle networking is proposed in this paper. This algorithm does not simply pursue the optimal distance between the virtual machine and the physical server performance vector, nor the minimum load of the data center, but combines the two factors flexibly through the reconciliation factor. Select the appropriate physical host for the business submitted by the user to deploy the corresponding virtual machine set. Experimental results under CloudSim environment show that the proposed algorithm can achieve good load balancing on the basis of satisfying personalized services.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09;TP391.44;TN929.5

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本文編號：2011428