【摘要】：云計算是IT領(lǐng)域的一次新的重大變革,推動了新的產(chǎn)業(yè)和價值鏈的發(fā)展。云計算平臺將大量的計算、存儲和網(wǎng)絡(luò)等資源,統(tǒng)一管理起來,并以網(wǎng)絡(luò)服務(wù)的方式提供給用戶,實現(xiàn)了資源協(xié)同共享,提高了資源的利用率。但是,由于云資源的規(guī)模龐大、異構(gòu)分布以及動態(tài)變化等特點,如何合理有效調(diào)度任務(wù),實現(xiàn)合理的資源分配面臨著很多的問題。針對云環(huán)境中工作流任務(wù)調(diào)度時資源節(jié)點的跨地域異構(gòu)性特點和執(zhí)行效率問題,以及數(shù)據(jù)處理類任務(wù)的調(diào)度問題,本文做出如下內(nèi)容的研究:(1)提出了云環(huán)境下基于人工免疫算法的工作流調(diào)度模型。云環(huán)境中資源的區(qū)域性分布對工作流任務(wù)調(diào)度過程中的任務(wù)節(jié)點間的通信傳輸產(chǎn)生很大的影響,本文針對這一問題,將工作流任務(wù)節(jié)點的完成時間和跨節(jié)點傳輸時延作為約束函數(shù),利用人工免疫算法的優(yōu)勢,由約束函數(shù)生成資源和任務(wù)間的適應(yīng)度函數(shù),并改進(jìn)人工免疫算法的抗體變異過程,采用基因重組方法進(jìn)行抗體的定向變異,提出一種基于人工免疫算法的工作流調(diào)度模型,以得到工作流任務(wù)節(jié)點和資源節(jié)點之間的合理調(diào)度方案。并通過實驗驗證了該算法在資源跨區(qū)域分布的工作流調(diào)度中可提高任務(wù)的執(zhí)行效率。(2)提出了云環(huán)境下基于數(shù)據(jù)分片的任務(wù)調(diào)度策略。針對數(shù)據(jù)處理類任務(wù)的調(diào)度過程,需要使用合理的調(diào)度方案將數(shù)據(jù)分片處理,并進(jìn)行征用資源,將數(shù)據(jù)分片分配到征用的資源節(jié)點上進(jìn)行數(shù)據(jù)處理,本文提出一種基于數(shù)據(jù)分片的任務(wù)調(diào)度策略,可依據(jù)數(shù)據(jù)的可切分粒度,以及可用資源節(jié)點的性能差異,建立數(shù)據(jù)分片的優(yōu)化調(diào)度數(shù)學(xué)模型,求解出數(shù)據(jù)分片的理想切分比例,再結(jié)合數(shù)據(jù)的實際可切分粒度,經(jīng)過二次分片可得到任務(wù)的調(diào)度策略。通過實驗驗證了采用該策略可有效縮短數(shù)據(jù)處理類任務(wù)的完成時間。(3)在基礎(chǔ)設(shè)施管理平臺的基礎(chǔ)上,設(shè)計并開發(fā)了數(shù)據(jù)服務(wù)云平臺,并在該平臺中基于本文提出的任務(wù)調(diào)度算法,實現(xiàn)了調(diào)度管理模塊。本文對調(diào)度管理模塊的計算中心、征用中心、數(shù)據(jù)中心及征用機(jī)服務(wù)做出了詳細(xì)的設(shè)計描述。通過系統(tǒng)測試,展示了本文提出的調(diào)度算法在資源征用場景中的效果。
[Abstract]:Cloud computing is a new major change in the field of IT, promoting the development of new industries and value chains. Cloud computing platform manages a large number of resources, such as computing, storage and network, and provides it to users in the way of network service. It realizes the cooperative sharing of resources and improves the utilization of resources. However, due to the large scale of cloud resources, heterogeneous distribution and dynamic changes, how to reasonably and effectively schedule tasks and achieve reasonable resource allocation is facing a lot of problems. In view of the cross-geographical heterogeneity and execution efficiency of resource nodes in workflow task scheduling in cloud environments, and the scheduling of data processing tasks, The main contents of this paper are as follows: (1) A workflow scheduling model based on artificial immune algorithm in cloud environment is proposed. The regional distribution of resources in cloud environment has a great influence on the communication transmission between task nodes in workflow task scheduling process. This paper aims at this problem. The completion time and cross-node transmission delay of workflow task nodes are regarded as constraint functions. Taking advantage of the advantage of artificial immune algorithm, the fitness function between resources and tasks is generated from the constraint function, and the antibody mutation process of artificial immune algorithm is improved. This paper presents a workflow scheduling model based on artificial immune algorithm to obtain a reasonable scheduling scheme between task nodes and resource nodes by means of gene recombination for directed variation of antibodies. Experiments show that the proposed algorithm can improve the efficiency of task execution in workflow scheduling with cross-regional distribution of resources. (2) A data slicing-based task scheduling strategy is proposed in the cloud environment. In view of the scheduling process of data processing tasks, it is necessary to use a reasonable scheduling scheme to segment the data and requisition the resources, and allocate the data fragments to the requisitioned resource nodes for data processing. In this paper, a task scheduling strategy based on data slicing is proposed. According to the granularity of data and the performance difference of available resource nodes, a mathematical model for optimal scheduling of data slicing is established, and the ideal slicing ratio of data is solved. Combined with the actual granularity of the data, the scheduling strategy of the task can be obtained through the secondary slicing. Experiments show that this strategy can effectively shorten the completion time of data processing tasks. (3) on the basis of infrastructure management platform, a data service cloud platform is designed and developed. In this platform, based on the task scheduling algorithm proposed in this paper, the scheduling management module is implemented. In this paper, the computing center, requisition center, data center and enlistment service of the dispatching management module are described in detail. Through the system test, it shows the effect of the proposed scheduling algorithm in the resource requisition scenario.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP3;TP18

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