本文選題：失效感知 + 工作流�。� 參考：《廣西師范大學(xué)》2017年碩士論文

【摘要】：近年來,由于云計算的快速發(fā)展,大規(guī)模的云計算數(shù)據(jù)中心在全球各地被廣泛建立。隨著對云計算關(guān)注度的提高,云計算的功能(functionality)和復(fù)雜度(complexity)被研究人員廣泛研究。在云環(huán)境中,為了在滿足服務(wù)質(zhì)量的前提下,盡可能的降低服務(wù)成本,實現(xiàn)用戶和服務(wù)提供商雙方最大化收益問題而引入了“云工作流”。通過將工作流技術(shù)與云計算相結(jié)合,一方面其可以將原先復(fù)雜的應(yīng)用需求按照業(yè)務(wù)邏輯進行抽象定義和分解,對任務(wù)和資源進行再次整理和靈活配置,從而提高服務(wù)質(zhì)量;另一方面其可以實現(xiàn)任務(wù)自動調(diào)度、任務(wù)監(jiān)控和資源分配的優(yōu)化與管理,因此可以大幅提高任務(wù)的執(zhí)行效率,能有效地提高云計算服務(wù)的質(zhì)量,減少執(zhí)行任務(wù)的費用開銷。針對云工作流的調(diào)度問題,不同的研究人員從完工時間最小化、執(zhí)行成本最低化、任務(wù)完成率最大化等多方面展開研究。雖然當(dāng)前存在很多云工作流的調(diào)度方法,但都沒有針對云計算資源失效而建立調(diào)度模型,從而有效規(guī)避和減少失效事件對云環(huán)境下工作流任務(wù)調(diào)度結(jié)果的影響。而在云計算環(huán)境中,資源失效是不可避免的。由于資源失效將直接帶來系統(tǒng)性能降低、程序執(zhí)行提前終止甚至數(shù)據(jù)丟失等問題,最終導(dǎo)致更多的任務(wù)不能在截止期內(nèi)完成、違約率增高,嚴(yán)重影響到云計算的可靠性和穩(wěn)定性,大大降低了服務(wù)質(zhì)量(Quality of Service,QoS)。同時由于工作流的各個任務(wù)之間存在時序約束和數(shù)據(jù)依賴。因此在工作流執(zhí)行的過程當(dāng)中,一旦某一個資源節(jié)點出現(xiàn)失效情況,不但導(dǎo)致此任務(wù)需要重新執(zhí)行,有可能整個工作流任務(wù)都需要重新執(zhí)行,嚴(yán)重影響到云計算的效率,浪費大量的計算資源�；诋�(dāng)前云計算環(huán)境下失效預(yù)測機制的國內(nèi)、外研究現(xiàn)狀和發(fā)展趨勢,結(jié)合云計算調(diào)度優(yōu)化特點,本文首先提出了基于失效感知的工作流調(diào)度模型,在調(diào)度過程中引入了失效預(yù)測機制和任務(wù)再調(diào)度策略,調(diào)度在滿足截止期要求的基礎(chǔ)上以最大化任務(wù)的完成率為目標(biāo)。在任務(wù)調(diào)度過程中,為工作流的每一個任務(wù)生成子截止時間。根據(jù)資源失效預(yù)測模型,當(dāng)所選的資源節(jié)點在任務(wù)的子截止期前發(fā)生失效時,提前把任務(wù)遷移到另一個可以順利完成該任務(wù)的節(jié)點上,從而有效地規(guī)避資源失效對任務(wù)執(zhí)行帶來的影響,且在任務(wù)遷移過程中盡量把關(guān)鍵路徑任務(wù)分配到同一個性能較高的虛擬機上以減少任務(wù)之間數(shù)據(jù)傳輸?shù)拈_銷,縮短完工時間,提高任務(wù)的完成率。然后對失效感知的工作流調(diào)度模型中的各個模塊功能進行了詳細(xì)說明,在此基礎(chǔ)上對失效預(yù)測機制、工作流模型和資源模型進行定義,最后對模型進行具體實現(xiàn),給出了基于失效感知的工作流調(diào)度算法(BFGA)。該算法基于遺傳算法進行改進,算法中提出了新穎的三元組編碼方式,在種群初始化過程中,采用隨機生成和使用已經(jīng)證明是有效的算法相結(jié)合的方式生成個體,以達(dá)到兼顧種群多樣性的目的。同時設(shè)計了符合工作流特點的交叉和變異方法,在個體進行交叉變異之后又引入了調(diào)整算子對部分結(jié)果進行局部微調(diào),以避免陷入局部最優(yōu),有效提高了收斂的速度。通過CloudSim云計算仿真平臺對提出的模型和算法進行仿真實驗,實驗借助不同類型工作流應(yīng)用和改變仿真環(huán)境參數(shù)的方法進行。通過與GA算法進行對比,驗證了算法的有效性。實驗證明BFGA算法相對一般的GA算法由于采用三元組的編碼方式,初始化種群采用了多種生成個體的方法,豐富了種群個體的多樣性,且在種群進化過程中增加了調(diào)整算子,使其具有更好的收斂速度。其次,從失效預(yù)測準(zhǔn)確率、工作流任務(wù)數(shù)量、失效節(jié)點比率三個方面來驗證BFGA算法與GA算法、First-fit算法、Pessimistic Best-fit算法以及不考慮失效的普通算法對任務(wù)調(diào)度的影響。實驗證明當(dāng)失效預(yù)測準(zhǔn)確率大于50%時,BFGA算法相比其他算法具有較高的作業(yè)完成率和不可靠節(jié)點利用率。當(dāng)失效預(yù)測準(zhǔn)確率為75%,工作流任務(wù)數(shù)大于600時五種算法的任務(wù)完成率均有下降,但是BFGA算法下降較為緩慢且一直高于其他四種算法。通過實驗有效地證明BFGA算法能夠降低資源失效給工作流任務(wù)調(diào)度帶來的影響,很好的解決了基于失效感知的工作流調(diào)度問題。
[Abstract]:In recent years, because of the rapid development of cloud computing, large cloud computing data centers have been widely established all over the world. With increasing attention to cloud computing, functionality and complexity are widely studied by researchers. In the cloud environment, in order to meet the quality of service, as much as possible By combining workflow technology with cloud computing, it can abstract and decompose the original complex application requirements according to the business logic, and re organize and configure the tasks and resources again, from the combination of workflow technology and cloud computing. To improve the quality of service, on the other hand, it can realize automatic task scheduling, task monitoring and resource allocation optimization and management, so it can greatly improve the efficiency of task execution, improve the quality of cloud computing services effectively and reduce the cost of execution tasks. The work time is minimized, the execution cost is minimized, and the task completion rate is maximized. Although there are many scheduling methods of cloud workflow, no scheduling model is established for the failure of cloud computing resources, thus effectively avoiding and reducing the impact of failure events on workflow task scheduling results under the cloud environment. In the cloud computing environment, resource failure is inevitable. Due to the failure of the resources, the performance of the system will be reduced, the execution of the program is terminated in advance or even the loss of the data. Finally, more tasks can not be completed in the deadline and the default rate is higher, which seriously affects the reliability and stability of the cloud computing and greatly reduces the service. Quality of Service (QoS). At the same time, due to the existence of temporal constraints and data dependence among the various tasks of the workflow, in the process of workflow execution, once a resource node fails, it not only causes the task to be re executed, but the whole workflow task needs to be re executed, and it is seriously affected. To the efficiency of cloud computing, a lot of computing resources are wasted. Based on the current situation and development trend of the domestic and external research on the failure prediction mechanism under the current cloud computing environment, combined with the characteristics of cloud computing scheduling optimization, this paper first proposes a workflow scheduling model based on failure aware, and introduces the failure prediction mechanism and task re tuning in the process of adjustment. On the basis of meeting the deadline requirements, scheduling is aimed at maximizing the completion rate of the task. In the task scheduling process, the sub cut-off time is generated for each task of the workflow. According to the resource failure prediction model, when the selected resource node fails before the sub deadline of the task, the task is moved to another one in advance. The task can be successfully completed on the node, thus effectively avoiding the impact of resource failure on task execution, and assigning the key path tasks to the same virtual machine with higher performance in the process of task migration to reduce the overhead of data transmission between tasks, shorten the completion time, and improve the completion rate of the task. The function of each module in the failure aware workflow scheduling model is explained in detail. On this basis, the failure prediction mechanism, the workflow model and the resource model are defined. Finally, the model is realized and the workflow scheduling algorithm based on the failure aware (BFGA) is given. The algorithm is improved based on the genetic algorithm. In the algorithm, a novel method of three tuple coding is proposed. In the process of population initialization, the individual is generated by combining random generation and using proven effective algorithms to achieve the purpose of taking into account the diversity of the population. At the same time, a cross and mutation method which conforms to the characteristics of the workflow is designed and after the individual crosses the mutation. In addition, the adjustment operator is introduced to local fine-tuning of partial results in order to avoid local optimum and improve the speed of convergence effectively. Through the simulation experiment of the proposed model and algorithm through the CloudSim cloud computing simulation platform, the experiment is carried out with the help of different types of workflow applications and changes of the real environment parameters. Through the GA algorithm, the experiment is carried out. The comparison shows the effectiveness of the algorithm. The experiment proves that the BFGA algorithm is relative to the general GA algorithm because of the use of three tuples, initializing the population using a variety of individual generation methods, enriching the diversity of the population, and increasing the adjustment operator in the process of population evolution, so that it has a better convergence rate. Secondly, From three aspects of the accuracy rate of failure prediction, the number of workflow tasks and the ratio of failure nodes, the effect of BFGA algorithm with GA algorithm, First-fit algorithm, Pessimistic Best-fit algorithm and the common algorithm without failure is verified. The experiment proves that when the accuracy rate of failure prediction is greater than 50%, the BFGA algorithm has a comparison with other algorithms. High job completion rate and unreliable node utilization rate. When the accuracy of failure prediction is 75% and the number of workflow tasks is more than 600, the task completion rate of the five algorithms is reduced, but the BFGA algorithm decreases slowly and has been higher than the other four algorithms. The experiment effectively proves that the BFGA algorithm can reduce the failure of resources to workflow The impact of job scheduling can solve the problem of workflow scheduling based on failure aware.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP301.6

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