發(fā)布時間：2018-04-17 17:09

  本文選題：云計算 + 資源調度��； 參考：《廣東工業(yè)大學》2014年碩士論文

【摘要】：云計算是一種由分布式計算、網(wǎng)格計算以及并行計算演變而來的新型計算模式。其主要運用虛擬化技術,將云端數(shù)據(jù)中心的各種資源虛擬化成為資源池進行管理以及對外提供服務,并且形成對用戶的“按需分配、按量計費”的商業(yè)模式。這些資源對用戶而言是透明的,用戶只需要知道云端數(shù)據(jù)中心所提供的服務并選擇自己需要的服務,不需要知道任務的具體執(zhí)行過程與具體執(zhí)行位置,云端把最終結果返回給用戶。云計算商業(yè)潛力巨大,對未來IT運營模式影響深遠,如今已成為國內外企業(yè)以及研究機構研究的熱點。 由于云計算環(huán)境的異構性、自治性、動態(tài)性以及云環(huán)境中使用了虛擬化技術,因此云計算環(huán)境中的資源分配方式跟以往的分布式計算、并行計算以及網(wǎng)格計算大不相同。為了適應云端數(shù)據(jù)中心規(guī)模的擴大和用戶以及任務數(shù)量的不斷增加,云環(huán)境下資源調度的目的在于提出一種優(yōu)化的資源調度策略,使得數(shù)據(jù)中心中的虛擬機資源能夠滿足用戶提出的Qos要求的同時又能實現(xiàn)資源的合理高效利用。 在簡要分析了云計算以及云環(huán)境下資源調度的研究現(xiàn)狀,總結了現(xiàn)有的資源調度策略的優(yōu)缺點以及改進方向,并介紹云計算與云環(huán)境下資源調度策略的相關概念以及技術體系的基礎上,本文主要做了以下三個方面的工作：第一,分析、研究傳統(tǒng)的蟻群算法在云計算環(huán)境下的資源調度上存在的問題,包括時間跨度大、負載均衡度較低以及優(yōu)化目標單一等：第二,在詳細介紹蟻群算法、模擬退火算法的基本思想、特點以及設計要素的基礎上,針對云計算編程最常用的Map/Reduce的框架,設計出一種新算法蟻群模擬退火算法(ACOSA),該算法融合蟻群算法以及模擬退火算法,以最小化調度時間為主要目標,引入了任務與資源的匹配因子和負載均衡度,利用蟻群算法得到一組任務到資源的優(yōu)化解,然后通過模擬退火算法,對解進行路徑的優(yōu)化和信息素的更新,最后得到全局最優(yōu)解；第三：在論文的最后,詳細地介紹了云計算仿真平臺CloudSim,對其進行重新編譯,實現(xiàn)了提出的本論文提出的ACOSA算法,通過與基于原始蟻群算法的云環(huán)境資源調度策略相比較,驗證了本文提出的調度策略在時間跨度以及負載均衡方面有良好的表現(xiàn)。
[Abstract]:Cloud computing is a new computing model evolved from distributed computing, grid computing and parallel computing.It mainly uses the virtualization technology to virtualize all kinds of resources of the cloud data center into the resource pool to manage and provide the service to the outside, and form the business mode of "according to demand, according to the quantity charge" to the user.These resources are transparent to users, who only need to know the services provided by the cloud data center and choose the services they need, without knowing the specific execution process and location of the task.The cloud returns the final result to the user.Cloud computing has great commercial potential and has a profound impact on the future IT business model. Now cloud computing has become a hot research topic for enterprises and research institutions at home and abroad.Because of the heterogeneity, autonomy, dynamics and virtualization technology used in cloud computing environment, resource allocation in cloud computing environment is very different from distributed computing, parallel computing and grid computing.In order to adapt to the expansion of the scale of cloud data center and the increasing number of users and tasks, the purpose of resource scheduling in cloud environment is to propose an optimized resource scheduling strategy.The virtual machine resources in the data center can meet the Qos requirements of users and realize the rational and efficient utilization of the resources at the same time.In this paper, the current situation of research on cloud computing and resource scheduling in cloud environment is briefly analyzed, and the advantages and disadvantages of existing resource scheduling strategies are summarized as well as the direction of improvement.Based on the introduction of cloud computing and resource scheduling strategy in cloud environment, this paper mainly does the following three aspects of work: first, analysis,This paper studies the problems of traditional ant colony algorithm in resource scheduling in cloud computing environment, including long time span, low load balance and single optimization goal. Secondly, the ant colony algorithm is introduced in detail.Based on the basic idea, characteristics and design elements of simulated annealing algorithm, a new ant colony simulated annealing algorithm (ACOSA) is designed for the framework of Map/Reduce, which is the most commonly used framework of cloud computing programming. The algorithm combines ant colony algorithm and simulated annealing algorithm.In order to minimize the scheduling time, the matching factor and load balancing degree of task and resource are introduced, and a set of optimal solution from task to resource is obtained by using ant colony algorithm, and then simulated annealing algorithm is used to solve the problem.Finally, the global optimal solution is obtained by optimizing the path of the solution and updating the pheromone. Thirdly, at the end of the paper, the cloud computing simulation platform CloudSims is introduced in detail, which is recompiled, and the ACOSA algorithm proposed in this paper is realized.Compared with the original ant colony algorithm based resource scheduling strategy in cloud environment, the proposed scheduling strategy has good performance in time span and load balancing.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP18;TP393.01

【參考文獻】

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本文編號：1764486