【摘要】：近幾年,云計(jì)算技術(shù)越來越成熟,并被成功運(yùn)用到了商業(yè)、教育、科研等領(lǐng)域,成為炙手可熱的計(jì)算機(jī)技術(shù)研究熱點(diǎn)。相較之前的Web服務(wù),云計(jì)算具有更高的可靠性、擴(kuò)展性和靈活性,它利用虛擬機(jī)、虛擬內(nèi)存等技術(shù)實(shí)現(xiàn)了虛擬化,形成了按需支付的模式,為服務(wù)提供商和終端用戶提供了諸多方便。在此基礎(chǔ)上,由計(jì)算機(jī)資源封裝而成的服務(wù)數(shù)量不斷增加并被發(fā)布到云計(jì)算平臺(tái)上為終端用戶提供服務(wù)。隨著網(wǎng)絡(luò)上可用服務(wù)數(shù)量的增加,用戶不再僅僅關(guān)注服是否可用,而是更加關(guān)注服務(wù)的質(zhì)量(Quality of Service,QoS),比如,執(zhí)行時(shí)間、花費(fèi)多少等。面對(duì)任務(wù)的繁多和用戶服務(wù)高質(zhì)量的需求,如何對(duì)云環(huán)境下的資源和任務(wù)進(jìn)行合理調(diào)度成為云計(jì)算研究的主要問題之一。另一方面,云計(jì)算系統(tǒng)中數(shù)據(jù)中心服務(wù)器數(shù)量多、服務(wù)資源異構(gòu)多樣、用戶基數(shù)大、用戶服務(wù)約束條件各不相同、應(yīng)用任務(wù)類型各異,云計(jì)算數(shù)據(jù)中必須能夠時(shí)刻可靠地處理海量用戶任務(wù)和數(shù)據(jù),如何及時(shí)高效并安全的將結(jié)果反饋給終端用戶并能滿足終端用戶的需求成為服務(wù)提供商面臨的最大挑戰(zhàn)。同時(shí),完成任務(wù)調(diào)度所產(chǎn)生的成本也是服務(wù)提供商最關(guān)心的問題之一。因此,高效的調(diào)度算法成為云環(huán)境下研究的重難點(diǎn)。在此基礎(chǔ)上,本文提出了一種改進(jìn)的蟻群算法,該算法綜合了計(jì)算資源(通常指虛擬機(jī)Virtual Machines,VMs)的可獲得性以及具有不同服務(wù)質(zhì)量(QoS)約束的任務(wù)的特性。鑒于傳統(tǒng)蟻群調(diào)度算法一般只考慮計(jì)算資源的特性,而忽略了用戶約束條件以及云資源的異構(gòu)性,本文中的算法將用戶任務(wù)分為計(jì)算密集型和網(wǎng)絡(luò)交互密集型兩種類型,并根據(jù)QoS優(yōu)先級(jí)和虛擬機(jī)處理速度分別對(duì)用戶任務(wù)和虛擬機(jī)進(jìn)行排序,旨在能夠在異構(gòu)環(huán)境中具有不同資源參數(shù)的計(jì)算資源上對(duì)具有不同服務(wù)質(zhì)量需求的任務(wù)進(jìn)行合理調(diào)度,以節(jié)約執(zhí)行時(shí)間和成本,同時(shí)滿足服務(wù)提供商和終端用戶的需求。實(shí)驗(yàn)結(jié)果表明,本文中提出的基于預(yù)測的調(diào)度算法更能傾向于找到最合理的任務(wù)虛擬機(jī)分配對(duì),并且反復(fù)執(zhí)行該算法的情況下能在一定程度上減少任務(wù)總執(zhí)行時(shí)間和成本。
[Abstract]:In recent years, cloud computing technology has become more and more mature, and has been successfully applied to business, education, scientific research and other fields, and has become a hot research hotspot of computer technology. Compared with the previous Web services, cloud computing has higher reliability, scalability and flexibility, it uses virtual machine, virtual memory and other technologies to achieve virtualization, forming an on-demand payment model, It provides a lot of convenience for service providers and end users. On this basis, the number of services encapsulated by computer resources continues to increase and is released to the cloud computing platform to provide services to end users. With the increase of the number of available services on the network, users are not only concerned about the availability of service, but also more about the quality of service, such as the execution time, the amount of time spent, and so on. In the face of various tasks and high quality user service, how to reasonably schedule resources and tasks in cloud environment has become one of the main problems in cloud computing research. On the other hand, the number of data center servers in cloud computing systems is large, the service resources are heterogeneous, the user base is large, the user service constraints are different, and the types of application tasks are different. Cloud computing data must be able to deal with massive user tasks and data reliably at all times. How to efficiently and safely feedback the results to end users and meet the needs of end users becomes the biggest challenge for service providers. At the same time, the cost of task scheduling is also one of the most concerned issues for service providers. Therefore, efficient scheduling algorithm has become a heavy and difficult problem in cloud environment. On this basis, an improved ant colony algorithm is proposed, which combines the availability of computing resources (usually referred to as virtual machine Virtual machines) and the properties of tasks with different quality of service (QoS) constraints. Since the traditional ant colony scheduling algorithm only considers the characteristics of computing resources, but ignores the user constraints and the heterogeneous nature of cloud resources, the algorithm in this paper divides user tasks into two types: computational intensive and network interaction intensive. According to the QoS priority and the processing speed of the virtual machine, the user tasks and the virtual machines are sorted respectively. The purpose of this paper is to schedule reasonably the tasks with different QoS requirements on the computing resources with different resource parameters in the heterogeneous environment. To save execution time and cost, while meeting the needs of service providers and end users. The experimental results show that the proposed scheduling algorithm based on prediction is more inclined to find the most reasonable task virtual machine allocation pairs and can reduce the total task execution time and cost to a certain extent when the algorithm is executed repeatedly.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP3;TP18

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本文編號(hào)：2153842