本文選題：云計(jì)算 + 工作流系統(tǒng)��； 參考：《安徽大學(xué)》2017年碩士論文

【摘要】：云計(jì)算是一種大型計(jì)算資源共享模型。云計(jì)算平臺(tái)在充分利用海量異構(gòu)分布式資源的同時(shí),可以向用戶提供無(wú)處不在、方便、按需的網(wǎng)絡(luò)計(jì)算資源服務(wù)。云計(jì)算的關(guān)鍵特征在于按需服務(wù)、超大規(guī)模、虛擬化、高可伸縮性和通用性。工作流是一種部分或完全由計(jì)算機(jī)自動(dòng)執(zhí)行的業(yè)務(wù)流程。工作流管理系統(tǒng)從用戶處接收任務(wù)且根據(jù)用戶的需求和任務(wù)限制條件為每個(gè)任務(wù)分配合適的資源。由于云計(jì)算的目標(biāo)是為用戶提供執(zhí)行效率更好且成本更低的資源,并且隨著在云環(huán)境中大規(guī)模電子商務(wù)以及科學(xué)計(jì)算等應(yīng)用的不斷發(fā)展,使得對(duì)云環(huán)境中任務(wù)自動(dòng)分配和執(zhí)行的QoS(QualityofService)目標(biāo)的要求不斷提升。因此,如何使得云環(huán)境中的任務(wù)調(diào)度和資源分配方案更加合理是一個(gè)重要的研究方向。云工作流系統(tǒng)是一種將云計(jì)算海量的資源配置與工作流的自主資源分配方法相結(jié)合的產(chǎn)物,云工作流管理系統(tǒng)根據(jù)工作流任務(wù)之間的依賴關(guān)系以及任務(wù)之間的優(yōu)先級(jí)將云計(jì)算中的各種可用資源分配給相應(yīng)的工作流任務(wù)。由于在云環(huán)境中資源的使用是有償?shù)?如果無(wú)法以一種合理的方式為這些任務(wù)分配合適的資源,那么將會(huì)增加云服務(wù)提供商的成本,同時(shí)也會(huì)使云環(huán)境中的各類資源無(wú)法得到充分利用。因此,如何通過(guò)云工作流系統(tǒng)為用戶所提交的任務(wù)分配合適的資源是一項(xiàng)十分重要的問(wèn)題。針對(duì)這一問(wèn)題,可以在云工作流系統(tǒng)中通過(guò)任務(wù)調(diào)度算法為不同任務(wù)分配合適的資源,早期的云環(huán)境由于規(guī)模不大,云服務(wù)提供商所最為關(guān)注的是任務(wù)執(zhí)行的費(fèi)用問(wèn)題,因此早期云環(huán)境中的任務(wù)調(diào)度算法優(yōu)化目標(biāo)為降低任務(wù)執(zhí)行費(fèi)用。隨著云計(jì)算的不斷發(fā)展,用戶對(duì)任務(wù)執(zhí)行完成時(shí)間的要求越來(lái)越高,云服務(wù)提供商也同時(shí)需要較高的資源利用率,此時(shí)調(diào)度算法的優(yōu)化目標(biāo)又轉(zhuǎn)移至降低任務(wù)執(zhí)行時(shí)間。近年來(lái)隨著云計(jì)算領(lǐng)域針對(duì)QoS優(yōu)化目標(biāo)的研究不斷興起,使得云工作流任務(wù)調(diào)度算法需要同時(shí)針對(duì)任務(wù)執(zhí)行的時(shí)間與費(fèi)用目標(biāo)進(jìn)行優(yōu)化。因此,如何將任務(wù)執(zhí)行時(shí)間與費(fèi)用兩個(gè)目標(biāo)有效的結(jié)合,進(jìn)而形成合適的QoS優(yōu)化目標(biāo)又成為了當(dāng)下研究的熱點(diǎn)。但是隨著近幾年來(lái)云計(jì)算行業(yè)的蓬勃發(fā)展以及巨型云數(shù)據(jù)中心的不斷出現(xiàn),云服務(wù)所帶來(lái)的巨額能耗成本在總運(yùn)營(yíng)成本中所占比重越來(lái)越大,如何優(yōu)化與管理大型云數(shù)據(jù)中心的能源消耗是一個(gè)巨大的挑戰(zhàn),通過(guò)云工作流管理系統(tǒng)可以管理和優(yōu)化云環(huán)境中的任務(wù)調(diào)度,降低服務(wù)器運(yùn)行能耗。然而,現(xiàn)有的云工作流管理系統(tǒng)針對(duì)能耗目標(biāo)優(yōu)化的研究較少,導(dǎo)致任務(wù)調(diào)度算法無(wú)法充分提高服務(wù)器資源利用率,降低任務(wù)執(zhí)行能耗。同時(shí)現(xiàn)有基于能耗的任務(wù)調(diào)度算法僅對(duì)任務(wù)執(zhí)行時(shí)的QoS需求或能耗目標(biāo)單獨(dú)進(jìn)行優(yōu)化。導(dǎo)致調(diào)度策略在優(yōu)化了服務(wù)器能耗的同時(shí),降低了云工作流服務(wù)性能指標(biāo)。這會(huì)造成云工作流無(wú)法滿足用戶在使用時(shí)的QoS需求。因此,如何在保證用戶QoS需求的同時(shí),盡可能降低任務(wù)執(zhí)行能耗是一個(gè)急需解決的問(wèn)題。目前云工作流系統(tǒng)常用的任務(wù)調(diào)度優(yōu)化算法為粒子群算法,然而傳統(tǒng)慣性權(quán)重的粒子群算法存在易陷入局部最優(yōu),迭代收斂速度緩慢的缺點(diǎn)。由此導(dǎo)致任務(wù)調(diào)度方案的費(fèi)用與能耗較高。因此,本文首先改進(jìn)了傳統(tǒng)自適應(yīng)慣性權(quán)重,新的自適應(yīng)慣性權(quán)重通過(guò)更加精確的描述粒子位置狀態(tài)以增強(qiáng)在算法迭代過(guò)程中對(duì)慣性權(quán)重的調(diào)整精度。接著提出了一種精細(xì)搜索的自適應(yīng)慣性權(quán)重粒子群算法(Fine Adaptive Inertia Weight-based Particle Swarm Optimization,FAIWPSO),然后將該算法分別針對(duì)云工作流系統(tǒng)任務(wù)調(diào)度方案的執(zhí)行費(fèi)用與能耗兩個(gè)目標(biāo)分別進(jìn)行優(yōu)化。提出了兩種任務(wù)調(diào)度算法:費(fèi)用優(yōu)化的粒子群任務(wù)調(diào)度算法與能耗感知的粒子群任務(wù)調(diào)度算法。本文的主要工作和創(chuàng)新點(diǎn)具體如下:1.針對(duì)傳統(tǒng)自適應(yīng)慣性權(quán)重的粒子群算法易陷入早熟與局部收斂的缺點(diǎn),對(duì)傳統(tǒng)自適應(yīng)慣性權(quán)重的成功值計(jì)算方法進(jìn)行改進(jìn),提出了一種精細(xì)搜索的自適應(yīng)慣性權(quán)重策略的粒子群算法。之后使用該算法對(duì)于云工作流任務(wù)調(diào)度執(zhí)行費(fèi)用與能耗目標(biāo)分別進(jìn)行了優(yōu)化研究。2.首先針對(duì)任務(wù)執(zhí)行的費(fèi)用目標(biāo)進(jìn)行研究。將精細(xì)搜索的自適應(yīng)慣性權(quán)重粒子群算法與云工作流任務(wù)層調(diào)度的費(fèi)用模型相結(jié)合提出了一種費(fèi)用優(yōu)化的自適應(yīng)慣性權(quán)重粒子群任務(wù)調(diào)度算法,對(duì)云工作流任務(wù)執(zhí)行費(fèi)用進(jìn)行優(yōu)化。通過(guò)將費(fèi)用優(yōu)化的自適應(yīng)慣性權(quán)重粒子群任務(wù)調(diào)度算法與其他五種不同慣性權(quán)重的粒子群算法實(shí)驗(yàn)對(duì)比,結(jié)果表明費(fèi)用優(yōu)化的自適應(yīng)慣性權(quán)重粒子群任務(wù)調(diào)度算法在算法收斂性、適應(yīng)度和任務(wù)執(zhí)行費(fèi)用三方面均優(yōu)于其余算法。3.接著針對(duì)任務(wù)執(zhí)行的能耗目標(biāo)進(jìn)行優(yōu)化研究。根據(jù)任務(wù)執(zhí)行能耗計(jì)算模型設(shè)計(jì)了適于評(píng)價(jià)任務(wù)調(diào)度方案執(zhí)行能耗的適應(yīng)度計(jì)算方法。之后結(jié)合精細(xì)搜索的自適應(yīng)粒子群任務(wù)調(diào)度算法提出了針對(duì)任務(wù)執(zhí)行能耗進(jìn)行優(yōu)化的能耗感知自適應(yīng)粒子群任務(wù)調(diào)度算法。通過(guò)與其他幾種慣性權(quán)重的粒子群算法進(jìn)行實(shí)驗(yàn)對(duì)比。結(jié)果表明,能耗感知自適應(yīng)粒子群任務(wù)調(diào)度算法不但收斂穩(wěn)定而且調(diào)度方案的執(zhí)行能耗最低。本文基于當(dāng)前針對(duì)云工作流任務(wù)調(diào)度的費(fèi)用與能耗問(wèn)題進(jìn)行了深入的研究。提出了一種精細(xì)搜索的自適應(yīng)慣性權(quán)重粒子群算法,分別針對(duì)當(dāng)前任務(wù)調(diào)度優(yōu)化目標(biāo)中兩個(gè)較為重要的目標(biāo)費(fèi)用與能耗分別進(jìn)行研究,提出了針對(duì)不同優(yōu)化目標(biāo)的兩種粒子群任務(wù)調(diào)度算法。最終通過(guò)實(shí)驗(yàn)證明了兩種算法不僅優(yōu)化了云工作流環(huán)境中的任務(wù)執(zhí)行費(fèi)用與能耗,而且在算法收斂穩(wěn)定性上均優(yōu)于對(duì)比算法。在降低了云環(huán)境中任務(wù)執(zhí)行的費(fèi)用與能耗的同時(shí),也利于我國(guó)的節(jié)能減排事業(yè)。因此本文在理論與實(shí)踐兩方面均具有重要意義。
[Abstract]:Cloud computing is a large computing resource sharing model. The cloud computing platform can provide users with ubiquitous, convenient and on-demand network computing resources while making full use of large and heterogeneous distributed resources. The key feature of cloud computing is that the key features of the cloud computing are on demand service, large scale, virtualization, high scalability and generality. A business process that is partially or completely automatically executed by a computer. A workflow management system receives tasks from the user and allocates appropriate resources for each task according to user needs and task constraints. Because the target of cloud computing is to provide users with better efficient and lower resources, and with the cloud environment. With the continuous development of large-scale e-commerce and scientific computing, the requirements of the QoS (QualityofService) target for task allocation and execution in the cloud environment are increasing. Therefore, how to make the task scheduling and resource allocation scheme more rational in the cloud environment is an important research direction. A product of combining a cloud computing resource configuration with an autonomous resource allocation method of workflow. The cloud workflow management system assigns all kinds of available resources in the cloud computing to the corresponding workflow tasks according to the dependencies between the tasks and the priority between tasks. It is paid, if it is not possible to allocate the appropriate resources for these tasks in a reasonable way, it will increase the cost of cloud service providers and also make the various resources in the cloud environment not fully utilized. Therefore, it is a very important way to allocate the appropriate resources for the tasks submitted by the user through the cloud workflow system. In order to solve this problem, the task scheduling algorithm can be used in the cloud workflow system to allocate the appropriate resources for different tasks. In the early cloud environment, because of the small size, the cloud service provider is most concerned about the cost of the task execution, so the task scheduling algorithm in the early cloud environment is optimized to reduce the task. With the continuous development of cloud computing, the demand for the completion time of task execution is getting higher and higher, and the cloud service provider also needs higher resource utilization. At this time, the optimization target of scheduling algorithm is transferred to the reduction of task execution time. In recent years, the research on the target of QoS optimization in the field of cloud computing is becoming more and more popular. It makes the cloud workflow task scheduling algorithm need to optimize the time and cost target of task execution. Therefore, how to combine the two goals of the task execution time and the cost effectively and then form the appropriate QoS optimization target has become the hot spot of the present research. However, with the vigorous development of the cloud computing industry in recent years. With the continuous appearance of the giant cloud data center, the huge energy cost of the cloud service is becoming more and more important in the total operating cost. How to optimize and manage the energy consumption of the large cloud data center is a huge challenge. Through the cloud workflow management system, it can manage and optimize the task scheduling in the cloud environment, and reduce the task scheduling in the cloud environment. However, the existing cloud workflow management system has less research on the optimization of energy consumption targets, which leads to the task scheduling algorithm can not fully improve the utilization of the server resources and reduce the task execution energy consumption. At the same time, the existing task scheduling algorithm based on energy consumption only carries out the QoS requirement or energy consumption target in the execution of the task alone. Optimization. It leads to the scheduling strategy to optimize the energy consumption of the server and reduce the performance indicators of the cloud workflow service. This will cause the cloud workflow not to meet the user's QoS requirements in use. Therefore, how to reduce the task execution energy consumption as much as possible while guaranteeing the user's QoS needs is an urgent problem. The common task scheduling optimization algorithm is particle swarm optimization (PSO), but the traditional particle swarm optimization (PSO) has the disadvantage of easy to fall into local optimal and slow convergence rate. Thus, the cost and energy consumption of the task scheduling scheme are higher. Therefore, this paper first improves the adaptive inertia weight and the new adaptive inertia weight. By describing the position state of the particle more accurately to enhance the precision of the adjustment of the inertia weight during the iterative process of the algorithm, a fine search adaptive inertia weight particle swarm optimization (Fine Adaptive Inertia Weight-based Particle Swarm Optimization, FAIWPSO) is then proposed, and the algorithm is then directed to the cloud workflow, respectively. The execution cost of the system task scheduling scheme and the two goals of energy consumption are optimized respectively. Two task scheduling algorithms are proposed: the particle swarm task scheduling algorithm and the energy aware particle swarm task scheduling algorithm for cost optimization. The main work and innovation points of this paper are as follows: 1. the particle swarm optimization for the traditional adaptive inertia weight is calculated. It is easy to fall into the shortcoming of precocious and local convergence, and improves the success value calculation method of the traditional adaptive inertia weight. A particle swarm optimization algorithm is proposed for the fine search adaptive inertia weight strategy. After that, the algorithm is used to optimize the.2. first for the execution cost and the energy consumption target of the cloud workflow task scheduling. Firstly, the cost target of task execution is studied. An adaptive inertia weight particle swarm optimization algorithm is proposed by combining the adaptive inertia weight particle swarm optimization algorithm with the cost model of cloud workflow task layer scheduling, which optimizes the execution cost of the cloud workflow task. The optimized adaptive inertia weight particle swarm task scheduling algorithm is compared with five other particle swarm optimization experiments with different inertia weights. The results show that the cost optimization of adaptive inertia weight particle swarm optimization algorithm is better than the other algorithm.3. in the algorithm convergence, the fitness and the task execution cost three. The energy consumption target of the line is optimized. According to the task execution energy calculation model, the fitness calculation method is designed to evaluate the energy consumption of the task scheduling scheme. Then the adaptive particle swarm optimization (PSO) task scheduling for the task execution energy consumption is proposed by combining the fine search adaptive particle swarm optimization task scheduling algorithm. The results show that the energy aware adaptive PSO task scheduling algorithm is not only convergent and stable, but also the energy consumption of the scheduling scheme is the lowest. This paper is based on the current research on the cost and energy consumption of cloud workflow task scheduling. A fine search adaptive inertia weight particle swarm optimization (PSO) is developed. According to the study of two important target costs and energy consumption respectively in the current task scheduling optimization target, two particle swarm task scheduling algorithms for different optimization targets are proposed. The final pass through experiment proves that the two algorithms not only optimize the cloud, but also optimize the cloud. The task execution cost and energy consumption in the workflow environment are better than the contrast algorithm in the convergence stability of the algorithm. It is also beneficial to the energy saving and emission reduction of our country while reducing the cost and energy consumption of the task execution in the cloud environment. Therefore, this paper is of great significance in two aspects of theory and practice.

【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】：2017
【分類號(hào)】：TP393.09;TP18

【共引文獻(xiàn)】

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2 彭二雄;;從“跨膜運(yùn)輸”看概念教學(xué)[J];中學(xué)生物教學(xué);2017年Z1期

3 楊華文;;例談基于思維培養(yǎng)的化學(xué)概念教學(xué)四個(gè)“點(diǎn)”[J];中小學(xué)教學(xué)研究;2017年01期

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本文編號(hào)：1846273