An Approach for Rule-Based Performance Evolutionary Optimization at Software Architecture Level

NI You-cong; YE Peng; DU Xin; CHEN Ming; XIAO Ru-liang

doi:10.3969/j.issn.0372-2112.2016.11.018

您当前的位置：

首页 >

文章列表页 >

An Approach for Rule-Based Performance Evolutionary Optimization at Software Architecture Level

更新时间：2025-07-16

- An Approach for Rule-Based Performance Evolutionary Optimization at Software Architecture Level
- Acta Electronica Sinica Vol. 44, Issue 11, Pages: 2688-2694(2016)
- 作者机构：
  
  1. 福建师范大学软件学院,福建,福州,350117
  2. 伦敦大学学院计算机科学学院,伦敦,英国,WC1E 6BT
  3. 武汉纺织大学数学与计算机学院,湖北,武汉,430200
  4. 湖南师范大学数学与计算机科学学院,湖南,长沙,410081
  5. 福建师范大学软件学院,福建,福州,350117
  6. 伦敦大学学院计算机科学学院,伦敦,英国,WC1E 6BT
  7. 武汉纺织大学数学与计算机学院,湖北,武汉,430200
  8. 湖南师范大学数学与计算机科学学院,湖南,长沙,410081
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.11.018
  CLC： TP311
- Published Online：25 November 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
NI You-cong, YE Peng, DU Xin, et al. An Approach for Rule-Based Performance Evolutionary Optimization at Software Architecture Level[J]. Acta Electronica Sinica, 2016, 44(11): 2688-2694.
DOI：

NI You-cong, YE Peng, DU Xin, et al. An Approach for Rule-Based Performance Evolutionary Optimization at Software Architecture Level[J]. Acta Electronica Sinica, 2016, 44(11): 2688-2694. DOI： 10.3969/j.issn.0372-2112.2016.11.018.

摘要

目前基于规则的软件体系结构（Software Architecture，简记为SA）层性能优化方法大多未充分考虑优化过程中规则的使用次数和使用顺序的不确定性，导致了搜索空间受限而难以获取更优的性能改进方案.针对这一问题并以最小化系统响应时间为优化目标，文中首先定义一种基于规则的SA层性能优化模型RPOM，以将SA层性能优化抽象为求解最优规则序列的数学问题；然后设计一种支持SA层性能改进规则序列执行的框架RSEF；进一步提出一种采用约束检查、修复及统计学习机制的演化求解算法EA4PO；最后以Web应用为案例与已有方法进行实验对比.结果表明：（1）本文方法较已有方法可以获取更短的系统响应时间；（2） EA4PO所引入的统计学习机制可显著提高演化求解算法的收敛速度和解质量.

Abstract

In the existing rule-based performance optimization approaches at software architecture (SA) level

it has not been fully concerned that the count and the order of each rule usage are uncertain in the optimization process.As a result

the search space for performance improvement is limited and the better solutions are hard to find.Aiming to this problem and taking the system response time minimum as the optimization objective

firstly

a model called RPOM is defined to abstract rule-based software performance optimization at SA level as the mathematical problem for solving the optimal rule sequence.Secondly

a framework named RSEF is designed to support the execution of a rule sequence.Furthermore

an evolutionary algorithm named EA4PO is proposed to find the optimal performance improvement solution by introducing statistical learning

constraint checking and repairing.Finally

a web application is taken as a case in the experiments for comparing with the existing methods.The experimental results indicate that the shorter system response time can be obtained and the statistical learning can obviously improve the convergence rate and the solution quality in our approach.

关键词

Keywords

references

Views

396

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Noise Filtering and Feature Enhancement Based Graph Neural Network Method for Fraud Detection

Reading and Writing Performance Optimization of Cross-Language FUSE Framework

An Efficient Algorithm of Information Recommendation Between Groups in Social Networks

PKUsim-86:A Cycle Level Full System Simulator for x86 Processor and AMBA-Based SoC

Formal Semantics of Architecture Model of Collaborative Systems

Related Author

HUANG Zhen-hua

LI Kang-he

HUANG Yi-hua

DAI Hai-peng

WANG Zhao-kang

QIU Ling-wei

LUO Yi-li

GU Rong

Related Institution

School of Computer Science， South China Normal University

School of Artificial Intelligence， South China Normal University

College of Computer Science and Technology， Nanjing University of Aeronautics and Astronautics

Department of Computer Science and Technology， Nanjing University

State Key Laboratory for Novel Software Technology， Nanjing University

⁰