發(fā)布時間：2018-06-08 09:54

  本文選題：Web系統(tǒng) + 參數(shù)優(yōu)化　； 參考：《華南理工大學(xué)》2014年碩士論文

【摘要】：互聯(lián)網(wǎng)深刻影響著我們的生活，日益增長的用戶流量給我們的Web系統(tǒng)提出了挑戰(zhàn)，要求我們充分利用Web系統(tǒng)的性能服務(wù)更多的用戶。通過調(diào)整Web系統(tǒng)的參數(shù)配置可以顯著地提升系統(tǒng)性能，但是由于Web系統(tǒng)的參數(shù)眾多，人工調(diào)整參數(shù)配置相當(dāng)麻煩，而且需要工作人員有豐富的經(jīng)驗。 本文通過分析自建Web系統(tǒng)，綜合各方面條件，利用二進制粒子群（BinaryParticle Swarm Optimization，BPSO）算法及其改進算法優(yōu)化Web系統(tǒng)的參數(shù)配置，尋找最優(yōu)參數(shù)配置，使Web系統(tǒng)性能表現(xiàn)最優(yōu)。本文主要包括以下內(nèi)容： （1）實驗平臺部署。經(jīng)過分析，選擇研究由Apache、MySQL和PHP組成的Web系統(tǒng)，選擇ApacheBench作為性能測試工具。采用Python編寫控制系統(tǒng)，實現(xiàn)Web系統(tǒng)和性能測試工具的連接，，完成實驗平臺部署。 （2）BPSO算法實現(xiàn)Web系統(tǒng)參數(shù)優(yōu)化。深入分析BPSO算法的基本原理和工作流程以及影響算法性能的因素，把它實際應(yīng)用到Web系統(tǒng)參數(shù)優(yōu)化問題中。針對Web系統(tǒng)的主要可調(diào)配置參數(shù)進行二進制編碼，采用計算機隨機的方法獲得初始種群，通過BPSO算法迭代優(yōu)化，獲得全局最優(yōu)解，給出實驗結(jié)果和算法性能分析。 （3）改進BPSO算法，并用其實現(xiàn)Web系統(tǒng)參數(shù)優(yōu)化。通過分析，BPSO算法容易早熟，前期粒子的多樣性過快降低，后期局部搜索能力弱。本文在算法早期通過引入耗散操作來增加粒子的多樣性，在算法后期通過引入爬山算法來增強算法的局部搜索能力，形成新的混合算法。用新的混合算法進行Web系統(tǒng)參數(shù)優(yōu)化，獲得全局最優(yōu)解，給出實驗結(jié)果和算法性能對比分析。 在有限的資源和負載下，本文通過部署實驗平臺進行Web系統(tǒng)性能測試實驗，運行算法程序找到系統(tǒng)最優(yōu)或接近最優(yōu)的參數(shù)配置，給出了實驗結(jié)果和分析，驗證優(yōu)化算法的有效性，有實際的應(yīng)用價值。
[Abstract]:The Internet has a profound impact on our daily life. The increasing user traffic challenges our Web system and requires us to make full use of the performance of the Web system to serve more users. By adjusting the parameter configuration of the Web system, the performance of the system can be significantly improved, but because of the large number of parameters of the Web system, the manual adjustment of the parameters is quite troublesome, and the staff is required to have rich experience. In this paper, the binary Particle Swarm Optimization (BPSO) algorithm and its improved algorithm are used to optimize the parameter configuration of the Web system and to find the optimal parameter configuration to optimize the performance of the Web system. This paper mainly includes the following contents: 1) deployment of experimental platform. After analysis, the Web system composed of Apache MySQL and PHP is studied, and Apache Bench is chosen as the performance testing tool. The control system is programmed by Python to realize the connection between the Web system and the performance testing tools, and the deployment of the experimental platform is completed, and the BPSO algorithm is used to optimize the parameters of the Web system. The basic principle and workflow of BPSO algorithm and the factors that affect the performance of BPSO algorithm are analyzed in detail, and the BPSO algorithm is applied to the optimization of Web system parameters. The binary coding for the main adjustable configuration parameters of Web system is carried out. The initial population is obtained by computer random method. The global optimal solution is obtained by iterative optimization of BPSO algorithm. The experimental results and performance analysis of the algorithm are given. The improved BPSO algorithm is improved. It is used to optimize the parameters of Web system. By analyzing that the BPSO algorithm is easy to prematurely reduce the diversity of particles too quickly and the local search ability is weak in the later stage. In this paper, dissipative operations are introduced to increase the diversity of particles in the early stage of the algorithm, and a new hybrid algorithm is formed by introducing the mountain climbing algorithm to enhance the local search ability of the algorithm. The new hybrid algorithm is used to optimize the parameters of the Web system, and the global optimal solution is obtained. The experimental results are compared with the performance of the algorithm. Under the limited resources and load, the performance of the Web system is tested by deploying the experimental platform. The algorithm program is run to find the optimal or near optimal parameter configuration of the system. The experimental results and analysis are given to verify the effectiveness of the optimization algorithm and it has practical application value.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09;TP18

【參考文獻】

相關(guān)期刊論文 前10條

1 鄧晶;張海全;王志堅;;Apache性能測試工具開發(fā)與研究[J];電腦知識與技術(shù);2011年04期

2 呂振肅,侯志榮;自適應(yīng)變異的粒子群優(yōu)化算法[J];電子學(xué)報;2004年03期

3 劉瑩;馬劍強;何挺;李保慶;褚家如;;模擬退火-爬山混合算法用于無波前傳感器快速像差校正[J];光學(xué)精密工程;2012年02期

4 楊紅孺,高洪元,龐偉正,張中兆;基于離散粒子群優(yōu)化算法的多用戶檢測器[J];哈爾濱工業(yè)大學(xué)學(xué)報;2005年09期

5 周友行,何清華,鄧伯祿;一種改進的爬山法優(yōu)化求解冗余機械手運動學(xué)逆解[J];機器人;2003年01期

6 邵芬;于國防;付海燕;;AB:一種簡單的性能測試工具[J];計算機時代;2007年12期

7 賀毅朝;王彥祺;劉建芹;;一種適于求解離散問題的二進制粒子群優(yōu)化算法[J];計算機應(yīng)用與軟件;2007年01期

8 王靈;俞金壽;;混沌耗散離散粒子群算法及其在故障診斷中的應(yīng)用[J];控制與決策;2007年10期

9 朱顥東;鐘勇;;基于并行二進制免疫量子粒子群優(yōu)化的特征選擇方法[J];控制與決策;2010年01期

10 陳恩修;劉希玉;;一種簡便高效的二元離散粒子群算法[J];控制與決策;2010年02期

本文編號：1995404