本文選題：云計(jì)算 + 面向服務(wù)的軟件�。� 參考：《中國科學(xué):信息科學(xué)》2017年06期

【摘要】：針對(duì)現(xiàn)有的部署優(yōu)化方法在求解云環(huán)境中面向服務(wù)軟件的部署優(yōu)化問題時(shí),無法處理服務(wù)實(shí)例和虛擬機(jī)實(shí)例的伸縮以及無法保障求解質(zhì)量等問題,本文提出了一種新的部署優(yōu)化方法.該方法以提高面向服務(wù)軟件的運(yùn)行性能和降低運(yùn)行成本為目標(biāo)構(gòu)建問題模型,并設(shè)計(jì)了一種基于遺傳算法的MGA-DO算法對(duì)其進(jìn)行求解.MGA-DO算法采用基于組的編碼方式對(duì)軟件的部署方案進(jìn)行編碼,然后結(jié)合基于組的單點(diǎn)交叉操作,實(shí)現(xiàn)了在優(yōu)化過程中對(duì)服務(wù)實(shí)例和虛擬機(jī)實(shí)例的伸縮.此外,該算法引入現(xiàn)有的部署優(yōu)化經(jīng)驗(yàn),設(shè)計(jì)了多種局部搜索規(guī)則,以進(jìn)一步提高算法的求解性能.最后,一系列模擬實(shí)驗(yàn)表明,相比現(xiàn)有的算法,MGA-DO算法在求解所研究的問題時(shí)表現(xiàn)出了更好的性能.
[Abstract]:In order to solve the problem of service-oriented software deployment optimization in cloud environment, the existing deployment optimization methods can not deal with the scalability of service instances and virtual machine instances, and can not guarantee the quality of solution. In this paper, a new deployment optimization method is proposed. The purpose of this method is to improve the performance and reduce the running cost of the service-oriented software. A genetic algorithm-based MGA-DO algorithm is designed to solve it. MGA-DO algorithm uses group-based coding to encode the deployment scheme of the software, and then combines the single-point crossover operation based on group. In the process of optimization, the service instance and virtual machine instance are scalable. In addition, the algorithm introduces the existing experience of deployment optimization and designs a variety of local search rules to further improve the performance of the algorithm. Finally, a series of simulation experiments show that the MGA-DO algorithm has better performance in solving the studied problems than the existing algorithms.
【作者單位】： 武漢大學(xué)軟件工程國家重點(diǎn)實(shí)驗(yàn)室;武漢大學(xué)計(jì)算機(jī)學(xué)院;
【基金】：國家高技術(shù)研究發(fā)展計(jì)劃(863)(批準(zhǔn)號(hào):2012AA011204) 國家自然科學(xué)基金(批準(zhǔn)號(hào):61373038、61672392)資助項(xiàng)目
【分類號(hào)】：TP18;TP302

【相似文獻(xiàn)】

相關(guān)期刊論文 前1條

1 ;新品走廊[J];微電腦世界;1999年49期

，

本文編號(hào)：2100010