SCIENTIA SINICA Informationis, Volume 47 , Issue 6 : 715(2017) https://doi.org/10.1360/N112016-00200

Evolutionary deployment optimization for service-oriented software in cloud}{Evolutionary deployment optimization for service-oriented software in cloud

Lin LI 1,2, Shi YING 1,2,*, Bo DONG 1,2, Rui WANG 1,2
More info
  • ReceivedNov 8, 2016
  • AcceptedJan 13, 2017
  • PublishedJun 6, 2017


Funded by






[1] Liu T, Lu T, Wang W, et al. SDMS-O: a service deployment management system for optimization in clouds while guaranteeing users' QoS requirements. Future Gener Comput Syst, 2012, 28: 1100-1109 CrossRef Google Scholar

[2] Armbrust M, Fox A, Griffith R, et al. A view of cloud computing. Commun ACM, 2010, 53: 50-58. Google Scholar

[3] Gu J, Luo J Z, Cao J X, et al. Performance modeling and analysis for composite service considering failure recovery. J Softw, 2013, 24: 696-714 [顾军,罗军舟,曹玖新,等.考虑失效恢复的组 合服务性能建模与分析.软件学报, 2013, 24: 696-714]. Google Scholar

[4] Mirandola R, Potena P, Scandurra P. Adaptation space exploration for service-oriented applications. Sci Comput Program, 2014, 80: 356-384 CrossRef Google Scholar

[5] Jennings B, Stadler R. Resource management in clouds: survey and research challenges. J Netw Syst Manage, 2015, 23: 567-619 CrossRef Google Scholar

[6] Canfora G, Di Penta M, Esposito R, et al. An approach for QoS-aware service composition based on genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, Washington, 2005. 1069-1075. Google Scholar

[7] Osman I H, Kelly J P. Meta-heuristics: an overview. In: Meta-Heuristics. Berlin: Springer, 1996. 1-21. Google Scholar

[8] Laili Y J, Zhang L, Tao F, et al. Rotated neighbor learning-based auto-configured evolutionary algorithm. Sci China Inf Sci, 2016, 59: 052101-619 CrossRef Google Scholar

[9] Aleti A, Buhnova B, Grunske L, et al. Software architecture optimization methods: a systematic literature review. IEEE Trans Softw Eng, 2013, 39: 658-683 CrossRef Google Scholar

[10] Aleti A, Grunske L, Meedeniya I, et al. Let the ants deploy your software-an ACO based deployment optimisation strategy. In: Proceedings of the 24th IEEE/ACM International Conference on Automated Software Engineering, Auckland, 2009. 505-509. Google Scholar

[11] Jayasinghe D, Pu C, Eilam T. Improving performance and availability of services hosted on IaaS clouds with structural constraint-aware virtual machine placement. In: Proceedings of the IEEE International Conference on Services Computing, Washington, 2011. 72-79. Google Scholar

[12] Malek S, Medvidovic N, Mikic-Rakic M. An extensible framework for improving a distributed software system's deployment architecture. IEEE Trans Softw Eng, 2012, 38: 73-100 CrossRef Google Scholar

[13] White J, Dougherty B, Thompson C, et al. ScatterD: spatial deployment optimization with hybrid heuristic/evolutionary algorithms. ACM Trans Auton Adap Syst, 2011, 6: 123-154. Google Scholar

[14] Zhang X W, Cao D G, Chen X Q, et al. Deployment solution optimization for mobile network application. J Softw, 2011, 22: 2866-2878 [张晓薇, 曹东刚, 陈向群, 等. 一种网络化移动应用部署方案优化方法. 软件学报, 2011, 22: 2866-2878]. Google Scholar

[15] Yusoh Z I M, Tang M. A cooperative coevolutionary algorithm for the composite SaaS placement problem in the cloud. In: Proceedings of International Conference on Neural Information Processing, Sydney, 2010. 618-625. Google Scholar

[16] Yusoh Z I M, Tang M. A penalty-based grouping genetic algorithm for multiple composite saas components clustering in cloud. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, 2012. 1396-1401. Google Scholar

[17] Yusoh Z I M, Tang M. Composite saas placement and resource optimization in cloud computing using evolutionary algorithms. In: Proceedings of IEEE 5th International Conference on Cloud Computing (CLOUD), Hawaii, 2012. 590-597. Google Scholar

[18] Zhao X T, Zhang B, Zhang C S. Service selection based resource allocation for SBS in cloud environments. J Softw, 2015, 26: 867-885 [赵秀涛, 张斌, 张长胜. 一种基于服务选取的 SBS 云资源优化分配方法. 软件学报, 2015, 26: 867-885]. Google Scholar

[19] Meng F C, Chu D H, Li K Q, et al. Solving SaaS components optimization placement problem with hybird genetic and simulated annealing algorithm. J Softw, 2016, 27: 916-932 [孟凡超, 初佃辉, 李克秋, 等. 基于混合遗传模拟退火算法的SaaS 构建优化放置方法. 软件学报, 2016, 27: 916-932]. Google Scholar

[20] Frey S, Fittkau F, Hasselbring W. Search-based genetic optimization for deployment and reconfiguration of software in the cloud. In: Proceedings of the 2013 International Conference on Software Engineering, San Francisco, 2013. 512-521. Google Scholar

[21] Wada H, Suzuki J, Yamano Y, et al. Evolutionary deployment optimization for service-oriented clouds. Softw Pract Exper, 2011, 41: 469-493 CrossRef Google Scholar

[22] Potena P. Optimization of adaptation plans for a service-oriented architecture with cost, reliability, availability and performance tradeoff. J Syst Softw, 2013, 86: 624-648 CrossRef Google Scholar

[23] Khazaei H, Mi\u sić J, Mi\u sić V B. Performance analysis of cloud computing centers using m/g/m/m+r queuing systems. IEEE Trans Parall Distr Syst, 2012, 23: 936-943 CrossRef Google Scholar

[24] Vilaplana J, Solsona F, Teixidó I, et al. A queuing theory model for cloud computing. J Supercomput, 2014, 69: 492-507 CrossRef Google Scholar

[25] Menasce D A, Dowdy L W, Almeida V A F. Performance by Design: Computer Capacity Planning by Example. Upper Saddle River: Prentice Hall, 2004. Google Scholar

[26] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-\uppercase\expandafter{\romannumeral2}. IEEE Trans Evolution Comput, 2002, 6: 182-197 CrossRef Google Scholar

[27] Yin H, Zhang C, Zhang B, et al. A hybrid multiobjective discrete particle swarm optimization algorithm for a sla-aware service composition problem. Math Problems Eng, 2014, 2014: 1-14. Google Scholar