發(fā)布時(shí)間：2018-06-14 12:27

  本文選題：集群 + 節(jié)能優(yōu)化��； 參考：《華中科技大學(xué)》2013年碩士論文

【摘要】：計(jì)算機(jī)集群的廣泛應(yīng)用給人們帶來(lái)方便的同時(shí)，也帶來(lái)了能耗問(wèn)題，如何有效地利用能源，降低不必要的能耗已成為一個(gè)迫切需要解決的關(guān)鍵問(wèn)題。作為目前最熱門(mén)的海量數(shù)據(jù)處理框架，Hadoop被部署到越來(lái)越多的集群上。但是Hadoop卻一直沒(méi)有引入節(jié)能的特性，因此對(duì)Hadoop集群的節(jié)能優(yōu)化技術(shù)進(jìn)行研究是非常有必要的。 基于現(xiàn)有的研究成果，結(jié)合Hadoop本身的機(jī)架感知特性和副本存放策略，提出了根據(jù)集群的負(fù)載情況調(diào)整集群規(guī)模來(lái)改善集群能源利用率的節(jié)能方案。節(jié)能方案為集群添加兩個(gè)功能模塊：集群負(fù)載監(jiān)控模塊和集群規(guī)模調(diào)整模塊。負(fù)載監(jiān)控模塊負(fù)責(zé)監(jiān)控集群的利用率，當(dāng)監(jiān)控到利用率過(guò)高或過(guò)低時(shí)會(huì)通知集群規(guī)模調(diào)整模塊對(duì)集群規(guī)模進(jìn)行調(diào)整。集群規(guī)模調(diào)整模塊包含兩個(gè)重要的算法：退役節(jié)點(diǎn)算法和重用節(jié)點(diǎn)算法，集群利用率過(guò)低時(shí)會(huì)啟動(dòng)退役節(jié)點(diǎn)算法，通過(guò)退役節(jié)點(diǎn)來(lái)減少集群中活躍節(jié)點(diǎn)數(shù)量，，以提高集群能源利用率，集群利用率過(guò)高時(shí)會(huì)啟動(dòng)重用節(jié)點(diǎn)算法來(lái)增加集群活躍節(jié)點(diǎn)數(shù)量，以提高集群對(duì)任務(wù)請(qǐng)求的響應(yīng)速度。 通過(guò)使用GridSim工具包來(lái)對(duì)系統(tǒng)進(jìn)行仿真實(shí)驗(yàn)，實(shí)驗(yàn)中對(duì)比了未使用節(jié)能策略的集群狀態(tài)和應(yīng)用節(jié)能策略的集群狀態(tài)，驗(yàn)證了退役節(jié)點(diǎn)算法和重用節(jié)點(diǎn)算法確實(shí)能夠根據(jù)集群的負(fù)載來(lái)改變集群的規(guī)模，實(shí)驗(yàn)結(jié)果表明在算法執(zhí)行過(guò)程中可以使集群節(jié)能30%以上。
[Abstract]:The extensive application of computer cluster brings people convenience and energy consumption. How to use energy effectively and reduce unnecessary energy consumption has become a key problem that needs to be solved urgently. Hadoop, the most popular mass data processing framework, is deployed to more and more clusters. However, Hadoop has not introduced the characteristics of energy saving, so it is necessary to study the energy-saving optimization technology of Hadoop cluster. Based on the existing research results, combined with Hadoop's own rack perception characteristics and replica storage strategy, an energy saving scheme is proposed to improve cluster energy efficiency by adjusting cluster scale according to the load situation of the cluster. The energy saving scheme adds two functional modules to the cluster: the cluster load monitoring module and the cluster scale adjustment module. The load monitoring module is responsible for monitoring the utilization rate of the cluster. When monitoring the utilization rate is too high or too low, the cluster size adjustment module will be notified to adjust the cluster size. The cluster scale adjustment module includes two important algorithms: the decommissioned node algorithm and the reuse node algorithm. When the cluster utilization is too low, the decommissioned node algorithm is started, and the number of active nodes in the cluster is reduced by decommissioned nodes. In order to improve the cluster energy utilization, if the cluster utilization is too high, the reuse node algorithm will be started to increase the number of active nodes in the cluster, so as to improve the response speed of the cluster to the task request. By using the GridSim toolkit to simulate the system, the cluster state without energy saving strategy and the cluster state with energy saving strategy are compared in the experiment. It is verified that the retired node algorithm and the reuse node algorithm can change the scale of the cluster according to the load of the cluster. The experimental results show that the algorithm can save more than 30% energy in the process of implementing the algorithm.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP333

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本文編號(hào)：2017378