本文選題：云計算 + 彈性��； 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：云計算作為一種正在興起的計算機(jī)科學(xué)發(fā)展方向，在當(dāng)前社會環(huán)境下?lián)碛芯薮蟮臐摿�。用戶通過云平臺得到服務(wù)和資源，并按其使用付費(fèi)，使得用戶得以以合理的費(fèi)用滿足其需求。 云計算之所以能夠引起廣泛的關(guān)注和應(yīng)用，最直接的驅(qū)動力量可以說是經(jīng)濟(jì)因素。通過租用云平臺中的基礎(chǔ)設(shè)施與相關(guān)軟件，用戶可以免于為其應(yīng)用提供基礎(chǔ)設(shè)施和維護(hù)，這大大降低了用戶應(yīng)用的成本。此外，由于云平臺還具有動態(tài)按需請求分配資源的特點(diǎn)，即彈性提供計算資源的能力，使得用戶得以進(jìn)一步控制其應(yīng)用代價。這使得云計算成為了更多人的選擇。 目前許多主流的商業(yè)云平臺，如Amazon EC2，Windows Azure等，都能夠在一定程度上提供彈性支持能力。例如，Amazon EC2的Auto Scaling服務(wù)可以與CloudWatch服務(wù)相配合以支持Amazon EC2實例數(shù)目的動態(tài)擴(kuò)展和收縮。用戶可以通過創(chuàng)建觸發(fā)器(trigger)自定義其應(yīng)用實例數(shù)目動態(tài)擴(kuò)展和收縮的規(guī)則。但這種彈性支持能力過于死板和僵硬，無法有效地做出更為精確的判斷。這種觸發(fā)器型的彈性支持能力雖然保證了用戶的應(yīng)用可以得到云平臺的彈性支持，但根據(jù)實際情況的不同，這種彈性支持能力可能會使用戶增加無謂的開銷。 若用戶的應(yīng)用希望以合理的代價得到較高的性能，一種恰當(dāng)?shù)馁Y源請求方案是必要的。通常,人工調(diào)節(jié)的方式往往是能收到良好效果的備選方案。但隨著云應(yīng)用的不斷擴(kuò)大，在不同的負(fù)載下，以人工的方式進(jìn)行判斷并為云應(yīng)用請求資源變得不再可行。為此，構(gòu)建一個彈性決策機(jī)制，使得此機(jī)制可以做出決策，保證服務(wù)質(zhì)量并節(jié)省開支，是一項有意義且富有挑戰(zhàn)性的工作。 針對此項問題，本文提出了一項彈性決策機(jī)制。此彈性決策機(jī)制在支持彈性能力的過程中建立系統(tǒng)負(fù)載模型和系統(tǒng)性能模型，并使用這兩種模型為用戶應(yīng)用做出彈性決策。與此同時，，為了防止這兩種模型失效而導(dǎo)致彈性決策機(jī)制喪失其本身的功能特性，此彈性決策機(jī)制在動態(tài)管理用戶應(yīng)用計算資源的同時，還將定時對系統(tǒng)負(fù)載模型和系統(tǒng)性能模型進(jìn)行有效性檢查。根據(jù)檢查的結(jié)果更新或重新建立模型。 在文章的末尾，本文將對此彈性決策模型的功能和性能進(jìn)行實驗驗證。本文所提出的彈性決策機(jī)制將根據(jù)實驗中給定的系統(tǒng)負(fù)載進(jìn)行彈性功能支持演示。并說明此彈性決策機(jī)制在工作中的特點(diǎn)。
[Abstract]:As an emerging computer science development direction , cloud computing has great potential in the current social environment . Users get services and resources through the cloud platform and pay for their use , so that users can meet their needs at reasonable cost .

Cloud computing can lead to a wide range of concerns and applications , and the most direct drive power can be said to be economic . By renting infrastructure and related software in a cloud platform , users can be free from providing infrastructure and maintenance for their applications , which significantly reduces the cost of user applications . In addition , cloud computing becomes a choice for more people because the cloud platform also has the feature of dynamically allocating resources on demand .

Currently , many mainstream business cloud platforms , such as Amazon 2 , Windows Azure , and so on , are able to provide some degree of resiliency support . For example , the Auto Scaling service of Amazon 2 can be matched to a dynamic expansion and contraction of the number of instances of Amazon . By creating triggers , users can customize the dynamic expansion and contraction of the number of instances of their applications . However , this resilient support capability is too inflexible and rigid to make more accurate judgments . This type of resilient support capability , while ensuring that the user ' s application can get the elastic support of the cloud platform , may increase the user ' s meaningless overhead , depending on the actual situation .

An appropriate resource request scheme is necessary if the user ' s application hopes to get higher performance at a reasonable cost . Generally , manual adjustment is often an alternative to good results . However , as cloud applications expand , it is no longer possible to make decisions in an artificial way and to request resources for cloud applications under different loads . To this end , an elastic decision - making mechanism is built so that this mechanism can make decisions , guarantee quality of service and save expenses , a meaningful and challenging task .

In order to prevent the failure of the two models , the elastic decision - making mechanism is used to make elastic decision - making . At the same time , in order to prevent the failure of the two models , the elastic decision - making mechanism loses its own function characteristic . At the same time , the elastic decision - making mechanism will also check the effectiveness of the system load model and the system performance model in order to prevent the failure of the two models .

At the end of this paper , the function and performance of this elastic decision - making model are verified experimentally . The elastic decision - making mechanism proposed in this paper will support the demonstration according to the system load given in the experiment .

【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 Yuan Tian;Chuang Lin;Zhen Chen;Jianxiong Wan;Xuehai Peng;;Performance Evaluation and Dynamic Optimization of Speed Scaling on Web Servers in Cloud Computing[J];Tsinghua Science and Technology;2013年03期

本文編號：1782795