本文關鍵詞： 工程優(yōu)化 Kriging 期望提高 信息熵 云計算 任務調度　出處：《大連理工大學》2015年博士論文　論文類型：學位論文

【摘要】：利用數(shù)據(jù)中心的高性能軟硬件資源,云計算能夠以“所付即所用”的方式對用戶提供高質量和高可靠的服務,這種“以服務的形式提供計算資源”的新型模式已經(jīng)在很多領域得到了廣泛運用。工程優(yōu)化問題具有復雜的應用背景,建設針對工程優(yōu)化應用的云計算平臺缺乏現(xiàn)有的成熟方法。首先,云平臺需要使用高性能的工程優(yōu)化方法完成云應用的開發(fā)和部署以節(jié)約用戶成本；其次,常規(guī)的云任務調度方法通常具有普適性,并不適應工程優(yōu)化云任務的調度需求；另外,工程優(yōu)化云應用響應時間和計算成本的密集特征也對用戶使用體驗和平臺資源分配提出了新要求。針對上述問題,本文展開了如下工作：1.提出了基于信息熵的期望提高(EEI)加點準則及其Kriging并行優(yōu)化方法。使用信息熵原理與加權形式的期望提高準則結合,可在每次優(yōu)化迭代時求得最優(yōu)加權系數(shù),使用該系數(shù)計算得到的樣本點可同時滿足優(yōu)化點的最大期望特征和加權系數(shù)的最優(yōu)特征；同時使用并行計算技術,按照投入運算的并行進程數(shù)分割樣本組和多點加點的加權系數(shù),可在較高的粒度上拆分整個優(yōu)化過程。使用EEI準則的Kriging并行優(yōu)化方法可以在保證優(yōu)化計算精度提高的同時,獲得理想的并行加速比,具有較高的計算性能。2.提出了基于Kriging代理模型的動態(tài)云任務調度方法。從工程優(yōu)化云應用的微觀計算特征出發(fā),針對其具有的計算平穩(wěn)期和過渡期特征,提出了使用Kriging代理模型對平穩(wěn)期進行計算資源優(yōu)化的策略。以每個計算平穩(wěn)期的資源分配組合為設計變量值,以任務響應時間和計算成本的多目標最小化為目標函數(shù)值,優(yōu)化計算后得到平穩(wěn)期的最優(yōu)資源分配方案。云任務的動態(tài)調度不僅有利于工程優(yōu)化云任務的快速計算和成本降低,也同時提高了云平臺計算資源的利用效率。3.提出了基于Kriging的云任務預測和分配方法并建設了工程優(yōu)化云平臺。針對工程優(yōu)化云應用的宏觀計算特征,構建了以應用部分和計算資源部分組成設計變量,以云任務的響應時間和計算成本為目標函數(shù)的優(yōu)化模型。利用Kriging建立的設計變量和響應時間函數(shù)關系,給出用戶的新任務響應時間預測值,任務成本給定條件下的計算資源分配為帶約束的響應時間最小化問題。響應時間的預測可以避免用戶不必要的等待過程,成本給定前提下計算資源的分配可以避免用戶使用平臺計算資源基于經(jīng)驗值的盲目性,也有助于用戶依據(jù)其計算成本總量合理安排各個計算任務。使用虛擬化技術創(chuàng)建了平臺組件,提出了核心功能的實現(xiàn)方式,最終建設了針對工程優(yōu)化應用的云計算平臺。
[Abstract]:Using the high performance hardware and software resources of the data center, cloud computing is able to deliver high-quality and reliable services to users in a "pay-as-you-go" manner, This new model of "providing computing resources in the form of services" has been widely used in many fields. Building cloud computing platform for engineering optimization application lacks existing mature methods. Firstly, cloud platform needs to use high-performance engineering optimization method to complete cloud application development and deployment to save user cost. Conventional cloud task scheduling methods are generally universal and do not meet the requirements of engineering optimization for cloud task scheduling; in addition, The dense features of response time and computing cost for engineering optimization cloud applications also put forward new requirements for user experience and platform resource allocation. In this paper, the following work is carried out: 1. The addition point criterion based on information entropy and its Kriging parallel optimization method are proposed. The information entropy principle is combined with the expectation enhancement criterion in weighted form. The optimal weighting coefficient can be obtained at each optimization iteration. The sample points obtained by using this coefficient can satisfy both the maximum expected characteristic of the optimization point and the optimal characteristic of the weighting coefficient, and the parallel computing technique is used. According to the number of parallel processes in the input operation, the whole optimization process can be split at a higher granularity by dividing the sample group and the weighting coefficients of the multi-point addition points. The Kriging parallel optimization method using the EEI criterion can ensure the improvement of the accuracy of the optimization calculation at the same time. A dynamic cloud task scheduling method based on Kriging agent model is proposed. Based on the microscopic computing characteristics of engineering optimization cloud application, the dynamic cloud task scheduling method is proposed. According to the characteristics of computing stationary period and transition period, a strategy of using Kriging agent model to optimize computing resources in stationary period is proposed. The resource allocation combination of each computing stationary period is taken as the design variable value. Taking the multi-objective minimization of task response time and computing cost as objective function value, the optimal resource allocation scheme for stationary period is obtained after optimization. The dynamic scheduling of cloud tasks is not only conducive to the rapid calculation and cost reduction of engineering optimization cloud tasks. At the same time, the utilization efficiency of cloud platform computing resources is improved. 3. A cloud task prediction and allocation method based on Kriging is proposed and an engineering optimization cloud platform is constructed. In this paper, an optimization model is constructed, which consists of application part and computing resource part, and takes the response time and computing cost of cloud task as objective function. The relationship between design variable and response time function is established by using Kriging. The prediction value of the user's new task response time is given. The computing resource allocation under the given task cost is a constrained response time minimization problem. The prediction of the response time can avoid the unnecessary waiting process of the user. The allocation of computing resources under a given cost can avoid the blindness of using platform computing resources based on empirical values. It also helps users reasonably arrange each computing task according to their total computing cost. Using virtualization technology, the platform components are created, and the core functions are implemented. Finally, the cloud computing platform for engineering optimization applications is built.
【學位授予單位】：大連理工大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TP393.09

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