Computer Science ›› 2017, Vol. 44 ›› Issue (11): 156-163, 174.doi: 10.11896/j.issn.1002-137X.2017.11.023

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Random Search Rule Based Performance Evolutionary Optimization Method at Software Architecture Level

NI You-cong, LI Song, YE Peng and DU Xin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The existing rule-based performance optimization approaches at software architecture (SA) level don’t fully concern the improvement range of the rule and the uncertainty of the count and the order of each rule usage in the optimization process.As a result,the search space for performance improvement is limited and the better solutions are hard to find.Aiming at this problem,firstly,a group of the random search rules (RSRs) were designed based on performance improvement tactics so that each RSR can explore the larger performance improvement space.Then the combination of the order and the count of each rule usage was involved in the definition of the random search rule based performance optimization model named RRPOM and the evolutionary algorithm was designed to solve RRPOM.Further,the random search rule based method for performance optimization at software architecture level (RRMO4PO) was proposed.Finally,a WebApp application was taken as a case in the experiments for comparing RRMO4PO with the existing methods.The experimental results show that RRMO4PO can obtain the solutions with better interpretability by using fewer rules and fewer times to modify the SA elements.In addition,the results also prove that both system response time and cost for performance improvement can be decreased more efficiently in our approach.In the best results in our experiments,the average number of rules with improvement effect and the times to modify the SA elements are reduced by 33.3% and 52.9% respectively,and the system response time and the cost for performance improvement are decreased by 30.5% and 73.6% respectively.

Key words: Software architecture,Performance optimization,System response time,Random search rule

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