【摘要】：自云計(jì)算概念被提出后,便迅速引發(fā)一場(chǎng)全球性的研究和開發(fā)熱潮,很多新興的云產(chǎn)品被紛紛推向市場(chǎng)。云計(jì)算是繼分布式處理、并行處理、網(wǎng)格計(jì)算、公用計(jì)算和虛擬化計(jì)算之后的一種按需來提供服務(wù)的計(jì)算模式。云計(jì)算利用虛擬化技術(shù)將數(shù)據(jù)中心底層物理資源抽象成虛擬單元,根據(jù)用戶的需求為其提供各類服務(wù)。為適應(yīng)用戶日益增加的需求,數(shù)據(jù)中心作為云計(jì)算的重要載體也在高速的膨脹,逐漸增加的IT設(shè)備使得云資源提供商及企業(yè)不得不考慮數(shù)據(jù)中心能耗、資源利用率、系統(tǒng)性能、服務(wù)質(zhì)量、負(fù)載、成本控制等問題。目前,云計(jì)算在資源調(diào)度方面的研究有幾個(gè)主要方向:以提高性能為目標(biāo)的資源調(diào)度,以提高服務(wù)質(zhì)量為目標(biāo)的資源調(diào)度,以經(jīng)濟(jì)原則為中心的資源調(diào)度,以提高資源利用率為目標(biāo)的資源調(diào)度,以降低能耗為目標(biāo)的資源調(diào)度,以平衡負(fù)載為目標(biāo)的資源調(diào)度。云系統(tǒng)中的資源類型有很多,比如CPU,內(nèi)存,硬盤,網(wǎng)絡(luò)帶寬,I/O設(shè)備等,且系統(tǒng)中的資源是動(dòng)態(tài)變化的。用戶向云端提交任務(wù),各個(gè)用戶提交的任務(wù)數(shù)量是不同的,且每個(gè)任務(wù)對(duì)資源的需求類型以及對(duì)每種類型資源的需求數(shù)量也是不同的。云系統(tǒng)中用戶和云提供商雙方利益角度不同,一個(gè)好的資源調(diào)度策略需要均衡各個(gè)參與者的利益。本文設(shè)計(jì)的基于偏好的公平分配策略FABP(Fair allocation strategy based on preference),給出了用戶優(yōu)先級(jí)和任務(wù)優(yōu)先級(jí)的定義,采用穩(wěn)定匹配理論來解決虛擬機(jī)置放問題。該算法在進(jìn)行任務(wù)調(diào)度時(shí)先通過用戶優(yōu)先級(jí)定位到某個(gè)用戶,再根據(jù)任務(wù)優(yōu)先級(jí)選擇該用戶相應(yīng)的任務(wù)進(jìn)行處理。在資源分配方面,先建立任務(wù)和物理機(jī)之間的偏好關(guān)系,再運(yùn)用穩(wěn)定匹配理論將任務(wù)置放到最合適的物理機(jī)上,以減少資源碎片的產(chǎn)生。實(shí)驗(yàn)結(jié)果表明,該算法不僅能縮短平均任務(wù)調(diào)度時(shí)間,而且保證了任務(wù)調(diào)度過程中用戶和任務(wù)的公平性,從而提高了用戶的滿意度,也實(shí)現(xiàn)了綜合資源利用率的最大化,從而保證了云資源提供商的利益。
[Abstract]:Since the concept of cloud computing was put forward, it has triggered a global research and development boom, and many new cloud products have been launched into the market. Cloud computing is an on-demand computing model following distributed processing, parallel processing, grid computing, common computing and virtualization computing. Cloud computing abstracts the underlying physical resources of the data center into virtual units using virtualization technology and provides various services according to the needs of users. In order to meet the increasing demand of users, data center as an important carrier of cloud computing is expanding at high speed. With the increasing of IT devices, cloud resource providers and enterprises have to consider data center energy consumption, resource utilization, system performance. Quality of service, load, cost control and so on. At present, the research on resource scheduling in cloud computing has several main directions: resource scheduling aiming at improving performance, resource scheduling aiming at improving quality of service, and resource scheduling centered on economic principle. Resource scheduling aims at improving resource utilization, resource scheduling aiming at reducing energy consumption, and resource scheduling aiming at balancing load. There are many resource types in cloud systems, such as CPU, memory, hard disk, network bandwidth, I / O devices and so on, and the resources in the system are dynamic. Users submit tasks to the cloud, and the number of tasks submitted by each user is different, and the types of requirements for resources and the number of requirements for each type of resources are different for each task. The interests of users and cloud providers are different in cloud systems. A good resource scheduling strategy needs to balance the interests of each participant. In this paper, we design a fair allocation strategy based on preference, FABP (Fair allocation strategy based on preference), which gives the definitions of user priority and task priority, and uses the theory of stable matching to solve the problem of virtual machine placement. The algorithm firstly locates a user through the priority of the user and then selects the corresponding task according to the priority of the task to process the task. In the aspect of resource allocation, the preference relationship between task and physics machine is established first, then the task is placed on the most suitable physical machine by using the theory of stable matching, so as to reduce the occurrence of resource fragment. Experimental results show that the algorithm can not only shorten the average task scheduling time, but also ensure the fairness of users and tasks in the task scheduling process. Thus ensuring the interests of cloud resource providers.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP3

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