本文選題：云計(jì)算 + 工作流系統(tǒng)　； 參考：《安徽大學(xué)》2014年碩士論文

【摘要】：伴隨著云計(jì)算的深入發(fā)展和研究,在云計(jì)算環(huán)境中開發(fā)的科學(xué)工作流,商務(wù)工作流以及協(xié)同應(yīng)用流程越來越多,他們功能強(qiáng)大且通常都需要大量的資源。同時(shí)在云環(huán)境中應(yīng)用服務(wù)流程變得越來越繁雜,此外還受到成本,時(shí)間及資源等因素的約束。通過可視化模型,云工作流系統(tǒng)可以靈活快速地構(gòu)建復(fù)雜流程,然后根據(jù)流程執(zhí)行和管理云計(jì)算應(yīng)用,從而使得云環(huán)境中的應(yīng)用服務(wù)能夠自行高效執(zhí)行。相比于其他的傳統(tǒng)計(jì)算環(huán)境,云環(huán)境是根據(jù)用戶需求獲取計(jì)算存儲(chǔ)資源并按使用量進(jìn)行付費(fèi)。因?yàn)樵朴?jì)算的特有的性質(zhì)導(dǎo)致傳統(tǒng)工作流的相關(guān)技術(shù)不能很好地解決云工作流管理中的問題。 資源分配和任務(wù)調(diào)度是云計(jì)算中兩個(gè)重要核心的技術(shù)。云工作流任務(wù)調(diào)度指的是在云環(huán)境中把用戶提交的工作流實(shí)例中的每個(gè)任務(wù)派分到合適的計(jì)算資源上進(jìn)行執(zhí)行并且對(duì)任務(wù)的運(yùn)行情況進(jìn)行管理,這能夠影響云工作流實(shí)例執(zhí)行的成功率以及高效性。相比于傳統(tǒng)環(huán)境中的調(diào)度,云工作流調(diào)度在進(jìn)行調(diào)度時(shí)不但要關(guān)注為任務(wù)選擇最優(yōu)的資源來符合預(yù)先定義好的調(diào)度約束(通�？紤]運(yùn)行時(shí)間和運(yùn)行成本),而且還要注意各個(gè)任務(wù)之間的先后依賴的約束條件,此外一定要協(xié)調(diào)各個(gè)任務(wù)的執(zhí)行情況來獲得最優(yōu)的執(zhí)行方案。云工作流調(diào)度通常是NP完全問題。 論文對(duì)云工作流任務(wù)層調(diào)度進(jìn)行深入研究,分析由底層資源虛擬化形成的虛擬機(jī)的分時(shí)特性,結(jié)合工作流任務(wù)的各類QoS約束,提出了基于虛擬機(jī)分時(shí)特性的任務(wù)層ACS調(diào)度算法。該算法考慮任務(wù)整體的成本約束,優(yōu)化執(zhí)行性能,同時(shí)考慮由底層資源虛擬化的虛擬機(jī)各自的性能,設(shè)定虛擬機(jī)允許最大并行數(shù)。由于云工作流任務(wù)層調(diào)度所面對(duì)的是集成工作流實(shí)例,每個(gè)任務(wù)的QoS約束更加復(fù)雜。我們針對(duì)諸多的約束設(shè)置多種啟發(fā)式信息。經(jīng)過仿真試驗(yàn),我們提出的算法相比于其他算法在對(duì)于較多并行任務(wù)的執(zhí)行上存在較大的優(yōu)勢(shì),能夠很好的利用虛擬的分時(shí)特性,優(yōu)化任務(wù)到虛擬機(jī)的調(diào)度。
[Abstract]:With the further development and research of cloud computing, there are more and more scientific workflow, business workflow and collaborative application processes developed in cloud computing environment. They are powerful and usually need a lot of resources. At the same time, the application of service processes in the cloud environment becomes more and more complicated, in addition to the constraints of cost, time and resources. Through the visualization model, the cloud workflow system can build complex processes flexibly and quickly, and then execute and manage cloud computing applications according to the process, so that the application services in the cloud environment can execute efficiently. Compared with other traditional computing environments, the cloud environment acquires the computing storage resources according to the user's needs and pays according to the usage. Because of the unique nature of cloud computing, the traditional workflow technology can not solve the problems in cloud workflow management. Resource allocation and task scheduling are two key technologies in cloud computing. Cloud workflow task scheduling refers to the assignment of each task in the workflow instance submitted by the user to the appropriate computing resources to execute and manage the performance of the task in the cloud environment. This can affect the success rate and efficiency of cloud workflow instance execution. Compared to scheduling in traditional environments, Cloud workflow scheduling should not only focus on selecting optimal resources for tasks to conform to pre-defined scheduling constraints (usually considering running time and running cost, but also on the order of tasks) Dependent constraints, In addition, it is necessary to coordinate the implementation of the various tasks to obtain the optimal implementation plan. Cloud workflow scheduling is usually a NP complete problem. In this paper, the task layer scheduling of cloud workflow is deeply studied, and the time-sharing characteristics of virtual machines formed by the virtualization of underlying resources are analyzed. Combined with various QoS constraints of workflow tasks, a task layer ACS scheduling algorithm based on the time-sharing characteristics of virtual machines is proposed. The algorithm takes into account the cost constraints of the task as a whole, optimizes the execution performance, and takes into account the performance of the virtual machines virtualized by the underlying resources, and sets the maximum number of parallel virtual machines allowed. Because the task layer scheduling of cloud workflow is faced with an integrated workflow instance, the QoS constraints of each task are more complex. We set up a variety of heuristic information for many constraints. The simulation results show that the proposed algorithm has more advantages than other algorithms in the execution of more parallel tasks and can make good use of the virtual time-sharing characteristics to optimize the scheduling of the task to virtual machine.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01

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