【摘要】：云計算是IT領域的一次新的重大變革,推動了新的產業(yè)和價值鏈的發(fā)展。云計算平臺將大量的計算、存儲和網絡等資源,統(tǒng)一管理起來,并以網絡服務的方式提供給用戶,實現了資源協同共享,提高了資源的利用率。但是,由于云資源的規(guī)模龐大、異構分布以及動態(tài)變化等特點,如何合理有效調度任務,實現合理的資源分配面臨著很多的問題。針對云環(huán)境中工作流任務調度時資源節(jié)點的跨地域異構性特點和執(zhí)行效率問題,以及數據處理類任務的調度問題,本文做出如下內容的研究:(1)提出了云環(huán)境下基于人工免疫算法的工作流調度模型。云環(huán)境中資源的區(qū)域性分布對工作流任務調度過程中的任務節(jié)點間的通信傳輸產生很大的影響,本文針對這一問題,將工作流任務節(jié)點的完成時間和跨節(jié)點傳輸時延作為約束函數,利用人工免疫算法的優(yōu)勢,由約束函數生成資源和任務間的適應度函數,并改進人工免疫算法的抗體變異過程,采用基因重組方法進行抗體的定向變異,提出一種基于人工免疫算法的工作流調度模型,以得到工作流任務節(jié)點和資源節(jié)點之間的合理調度方案。并通過實驗驗證了該算法在資源跨區(qū)域分布的工作流調度中可提高任務的執(zhí)行效率。(2)提出了云環(huán)境下基于數據分片的任務調度策略。針對數據處理類任務的調度過程,需要使用合理的調度方案將數據分片處理,并進行征用資源,將數據分片分配到征用的資源節(jié)點上進行數據處理,本文提出一種基于數據分片的任務調度策略,可依據數據的可切分粒度,以及可用資源節(jié)點的性能差異,建立數據分片的優(yōu)化調度數學模型,求解出數據分片的理想切分比例,再結合數據的實際可切分粒度,經過二次分片可得到任務的調度策略。通過實驗驗證了采用該策略可有效縮短數據處理類任務的完成時間。(3)在基礎設施管理平臺的基礎上,設計并開發(fā)了數據服務云平臺,并在該平臺中基于本文提出的任務調度算法,實現了調度管理模塊。本文對調度管理模塊的計算中心、征用中心、數據中心及征用機服務做出了詳細的設計描述。通過系統(tǒng)測試,展示了本文提出的調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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP3;TP18

【參考文獻】

相關期刊論文 前9條

1 黃婷婷;梁意文;;云工作流任務調度的模擬退火遺傳改進算法[J];微電子學與計算機;2016年01期

2 魏峗;陳元元;;基于改進蟻群算法的云計算任務調度模型[J];計算機工程;2015年02期

3 楊鏡;吳磊;武德安;王曉敏;劉念伯;;云平臺下動態(tài)任務調度人工免疫算法[J];計算機應用;2014年02期

4 封良良;夏曉燕;賈振紅;張?zhí)?;實驗基于資源預先分類的云計算任務調度算法[J];計算機仿真;2013年10期

5 郭鳳羽;禹龍;田生偉;于炯;孫華;;云計算環(huán)境下對資源聚類的工作流任務調度算法[J];計算機應用;2013年08期

6 丁丁;羅四維;高瞻;;一種基于任務劃分的啟發(fā)式網格調度策略[J];北京交通大學學報;2007年05期

7 謝克明,郭紅波,謝剛,續(xù)欣瑩;人工免疫算法及其應用[J];計算機工程與應用;2005年20期

8 吳青,劉三陽,張樂友;Minimax問題的調節(jié)熵函數法[J];經濟數學;2005年02期

9 楊慶之;解極小極大問題的熵函數法[J];南開大學學報(自然科學版);2001年03期

，

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