Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 286-292.doi: 10.11896/j.issn.1002-137X.2017.11A.061

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Research on Deployment Decision Method of Virtual Application Network in Multi-cloud Environment

ZHU Hua-min, WU Li-fa and ZHAO Peng   

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

Abstract: In multi-cloud environment,users can freely combine infrastructure resources to deploy virtual application network based on virtual application technology and infrastructure virtualization technology,and thus quickly build a distributed application system with certain business functions.Regarding the shortcomings of the existing multi-cloud deployment decision-making methods in deployment descriptions and addressing user multi-objective requirements,this paper first presented a deployment description method for the virtual application network based on the open virtual format document.Secondly,the common service quality metrics of the infrastructure resource combination were studied,and a multi-objective optimization model of the combination was subsequently defined.Then,the second generation non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm were used to solve the model.Finally,a fuzzy decision-making method was designed to select the final satisfactory solution from the obtained results.The statistics of multiple experiments show that both algorithms can achieve a good convergence within a reasonable time,NSGA-II is only suitable for scenes with 2 to 3 objectives,and MOPSO can be used for scenes with more objectives and performs better than NSGA-II.Moreover,the fuzzy decision-making result can match the user’s objective preference best.

Key words: Multi-cloud,Virtual application network,Infrastructure as a service,Multi-objective optimization,Fuzzy decision-making

[1] GROZEV N,BUYYA R.Inter-Cloud architectures and application brokering:taxonomy and survey[J].Software:Practice and Experience,2014,44(3):369-390.
[2] PETCU D.Multi-Cloud:expectations and current approaches[C]∥Proceedings of the 2013 International Workshop on Multi-cloud Applications and Federated Clouds.ACM,2013.
[3] FERRER A J.Inter-cloud Research:Vision for 2020[J].Procedia Computer Science,2016,97:140-143.
[4] ASSIS M,BITTENCOURT L.A survey on cloud federation architectures:Identifying functional and non-functional properties[J].Journal of Network and Computer Applications,2016,72:51-71.
[5] TORDSSON J,MONTERO R S,MORENO-VOZMEDIANO R,et al.Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers[J].Future Gene-ration Computer Systems,2012,28(2):358-367.
[6] QAVAMI H R,JAMALI S,AKBARI M K,et al.Dynamic resource provisioning in cloud computing:a heuristic markovian approach[C]∥International Conference on Cloud Computing.Springer,2013.
[7] MENZEL M,RANJAN R,WANG L,et al.CloudGenius:a hybrid decision support method for automating the migration of web application clusters to public clouds[J].IEEE Transactions on Computers,2015,64(5):1336-1348.
[8] ZHU H M,WU L F,HUANG K Y,et al.Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling.http://downloads.hirdawi.com/journals/mpe/2016/8194832.pdf.
[9] CROSBY S,DOYLE R,GERING M,et al.Open virtualization format specification[R].Distributed Management Task Force,2010.
[10] DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE transactions on evolutionary computation,2002,6(2):182-197.
[11] COELLO C A C,PULIDO G T,LECHUGA M S.Handlingmultiple objectives with particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[12] PAWLUK P,SIMMONS B,SMIT M,et al.Introducing STRATOS:A cloud broker service[C]∥2012 IEEE 5th International Conference on Cloud Computing (CLOUD).IEEE,2012.
[13] PETCU D,DI MARTINO B,VeENTICINQUE S,et al.Experiences in building a mOSAIC of clouds[J].Journal of Cloud Computing:Advances,Systems and Applications,2013,2(1):12.
[14] KESSACI Y,MELAB N,TALBI E G.A pareto-based geneticalgorithm for optimized assignment of vm requests on a cloud brokering environment [C]∥2013 IEEE Congress on Evolutionary Computation (CEC).IEEE,2013.
[15] SIEGEL J,PERDUE J.Cloud services measures for global use:the Service Measurement Index (SMI)[C]∥2012 Annual SRII Global Conference (SRII).IEEE,2012.
[16] LI Z,O’BRIEN L,ZHANG H,et al.On a catalogue of metrics for evaluating commercial cloud services[C]∥Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing.IEEE Computer Society,2012.
[17] GARG S K,VERSTEEG S,BUYYA R.A framework for ran-king of cloud computing services[J].Future Generation Computer Systems,2013,29(4):1012-1023.
[18] SPEC.http://www.spec.org.
[19] Cloudharmony.http://www.cloudharmony.com.
[20] 朱华旻,吴礼发,康红凯.基于SecLA的云服务商选择方法研究[J].计算机科学,2016,43(5):100-107.
[21] 公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289.
[22] 胡旺,YEN G,张鑫.基于Pareto熵的多目标粒子群优化算法[J].软件学报,2014(5):1025-1050.
[23] Rackspace.http://www.rackspace.com.
[24] Digitalocean.http://www.digitalocean.com.
[25] Linode.http://www.linode.com.
[26] ZITZLER E,DEB K,THIELE L.Comparison of multiobjective evolutionary algorithms:Empirical results[J].Evolutionary computation,2000,8(2):173-195.
[27] SCHOTT J R.Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization[J].Cellular Immunology,1995,37(1):1-13.
[28] SBALZARINI I F,MLLER S,KOUMOUTSAKOS P.Mul-tiobjective optimization using evolutionary algorithms[C]∥Proceedings of the Summer Program.CiteSeer,2000.
[29] Minitab.http://www.minitab.com/en-us.

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