本文選題：資源管理　切入點(diǎn)：協(xié)同監(jiān)控　出處：《南京郵電大學(xué)》2017年碩士論文

【摘要】：隨著云計(jì)算不斷發(fā)展,數(shù)據(jù)中心承載的交換機(jī)和服務(wù)器等硬件設(shè)備越來(lái)越多,海量的資源帶來(lái)了巨大的管理壓力,威脅著云平臺(tái)的健康、穩(wěn)定運(yùn)行。在云資源管理中一直存在著兩個(gè)問(wèn)題,一方面為了協(xié)調(diào)任務(wù)的部署,提高系統(tǒng)的執(zhí)行效率和穩(wěn)定性,云系統(tǒng)需要高效的節(jié)點(diǎn)資源監(jiān)控機(jī)制。然而傳統(tǒng)的集中式監(jiān)控架構(gòu)容易導(dǎo)致單點(diǎn)失效和性能瓶頸等問(wèn)題,不能適應(yīng)大規(guī)模的云環(huán)境。另一方面,復(fù)雜且繁重的業(yè)務(wù)為云數(shù)據(jù)中心帶來(lái)了沉重的網(wǎng)絡(luò)負(fù)載,同時(shí)網(wǎng)絡(luò)負(fù)載不均衡導(dǎo)致了資源利用率低、時(shí)延長(zhǎng)、靈活性與可靠性差、吞吐率不理想等問(wèn)題,傳統(tǒng)的網(wǎng)絡(luò)架構(gòu)或簡(jiǎn)單增加新的網(wǎng)絡(luò)資源等方式不能有效解決問(wèn)題,因此為有效提高云數(shù)據(jù)中心網(wǎng)絡(luò)資源利用率和服務(wù)質(zhì)量,降低運(yùn)營(yíng)成本,特別是解決網(wǎng)絡(luò)部分擁塞、部分利用率低以及可靠性等問(wèn)題,亟需構(gòu)建一套適應(yīng)于云系統(tǒng)的網(wǎng)絡(luò)管理機(jī)制。針對(duì)這兩個(gè)問(wèn)題,本文從節(jié)點(diǎn)資源監(jiān)控角度提出了一種新型的分布式協(xié)同監(jiān)控機(jī)制,包括一種分布式協(xié)同監(jiān)控模型和一種自適應(yīng)監(jiān)控閾值控制算法,從網(wǎng)絡(luò)資源管理角度提出了基于SDN(Software Defined Network)的多路徑流量調(diào)度機(jī)制。具體的研究工作如下:(1)提出了一種面向云計(jì)算系統(tǒng)的分布式協(xié)同監(jiān)控模型(Distributed Collaborative Monitoring Model,DCMM),以數(shù)據(jù)節(jié)點(diǎn)相互感知、彼此監(jiān)控的方式,實(shí)現(xiàn)正常狀態(tài)下數(shù)據(jù)節(jié)點(diǎn)的自我管理,異常狀態(tài)下向主節(jié)點(diǎn)的及時(shí)信息推送,從而均衡監(jiān)控負(fù)載,避免單點(diǎn)失效和性能瓶頸。(2)提出了一種自適應(yīng)閾值控制算法(Adaptive Threshold Control Algorithm,ATCA),基于歷史監(jiān)控?cái)?shù)據(jù),動(dòng)態(tài)地調(diào)整閾值以識(shí)別沒(méi)必要推送至監(jiān)控節(jié)點(diǎn)的重復(fù)監(jiān)控?cái)?shù)據(jù),進(jìn)而減少監(jiān)控?cái)?shù)據(jù)傳輸,減輕監(jiān)控系統(tǒng)對(duì)整個(gè)系統(tǒng)的影響。(3)提出了一種面向云系統(tǒng)網(wǎng)絡(luò)的基于SDN架構(gòu)的多路徑流量調(diào)度機(jī)制(Multi-path Traffic Scheduling mechanism based on SDN,MTSS),利用網(wǎng)絡(luò)負(fù)載均衡算法為新加入的數(shù)據(jù)流選擇負(fù)載最輕的路徑,并周期性監(jiān)控網(wǎng)絡(luò)各鏈路帶寬利用情況,自適應(yīng)地根據(jù)鏈路負(fù)載情況,充分利用網(wǎng)絡(luò)中空閑鏈路,制定合適的路由轉(zhuǎn)發(fā)路徑,調(diào)度負(fù)載較重鏈路上的流量,實(shí)現(xiàn)靈活的可編程式數(shù)據(jù)轉(zhuǎn)發(fā)以均衡網(wǎng)絡(luò)負(fù)載。MTSS能夠有效提高了網(wǎng)絡(luò)的負(fù)載均衡性、資源利用率,從而有效降低網(wǎng)絡(luò)丟包率、時(shí)延,提高網(wǎng)絡(luò)的可靠性和吞吐率。(4)在協(xié)同監(jiān)控和流量調(diào)度機(jī)制的基礎(chǔ)上,設(shè)計(jì)并構(gòu)建了云資源管理系統(tǒng),詳細(xì)闡述了系統(tǒng)的架構(gòu),并分別根據(jù)系統(tǒng)界面介紹了各功能和具體效果。
[Abstract]:With the continuous development of cloud computing, more and more hardware devices such as switches and servers are loaded in the data center. Massive resources bring huge management pressure, threatening the health of cloud platform and running stably.There are two problems in cloud resource management. On the one hand, in order to coordinate the deployment of tasks and improve the efficiency and stability of the system, the cloud system needs an efficient monitoring mechanism of node resources.However, the traditional centralized monitoring architecture can easily lead to single point failure and performance bottlenecks, and can not adapt to large-scale cloud environment.On the other hand, the complex and heavy business brings heavy network load to the cloud data center. At the same time, the network load imbalance results in low resource utilization, prolonged time, poor flexibility and reliability, poor throughput and so on.Traditional network architecture or simply adding new network resources can not effectively solve the problem. Therefore, in order to effectively improve the utilization of network resources and the quality of service of cloud data center, reduce the operation cost, especially solve the network congestion.Due to some problems such as low utilization and reliability, it is urgent to construct a network management mechanism suitable for cloud systems.Aiming at these two problems, this paper proposes a new distributed cooperative monitoring mechanism from the point of view of node resource monitoring, including a distributed cooperative monitoring model and an adaptive threshold control algorithm.A multi-channel runoff scheduling mechanism based on SDN(Software Defined Network is proposed from the point of view of network resource management.The specific research work is as follows: (1) A distributed Collaborative Monitoring Model DCMMN (distributed Collaborative Monitoring Model) for cloud computing system is proposed to realize the self-management of data nodes under normal condition by mutual perception and mutual monitoring of data nodes.In order to balance the monitoring load and avoid single point failure and performance bottleneck, this paper presents an adaptive Threshold Control algorithm based on historical monitoring data.Dynamically adjust the threshold to identify repetitive monitoring data that is not necessarily pushed to the monitoring node, thereby reducing the transmission of monitoring data,In this paper, we propose a multi-path Traffic Scheduling mechanism based on SDN MTSS scheduling mechanism based on SDN architecture for cloud system network, and use the network load balancing algorithm to select the new data stream. (3) to reduce the impact of monitoring system on the whole system, we propose a multi-path Traffic Scheduling mechanism based on SDN MTSS scheduling mechanism based on SDN architecture for cloud system network.The lightest load path,And periodically monitor the use of each link bandwidth, adaptively according to the link load, make full use of the free link in the network, make appropriate routing and forwarding path, and schedule the traffic on the heavy load link.The realization of flexible programmable data forwarding to balance network load. MTSS can effectively improve the load balance and resource utilization of the network, thus effectively reduce the packet loss rate and delay.On the basis of cooperative monitoring and traffic scheduling mechanism, the cloud resource management system is designed and constructed. The architecture of the system is described in detail, and each function and concrete effect are introduced according to the system interface.
【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(lèi)號(hào)】：TP393.07

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