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Home > Archive>Volume 27, Issue 4, 2016 >916-932. DOI:10.13328/j.cnki.jos.004965
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Solving SaaS Components Optimization Placement Problem with Hybird Genetic and Simulated Annealing Algorithm
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National Key R&D Plan Project of China (2014BAF07B02); National Natural Science Foundation of China (61432002); Major Scienece & Technology Specific Project of Shandong Province (2015ZDXX0201B02); Natural Science Foundation of Shandong Province (2015ZRA10032)

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    Abstract:

    Current researches on SaaS(software as a service) optimization placement mostly assume that the types and number of virtual machines are constant in cloud environment, namely, the optimization placement is based on the restricted resource. However, in actual situation the types and number of virtual machines are unknown, and they need to been calculated according to the resource requirement of components deployed. To address the issue, from the view of SaaS providers, this paper proposes a new approach to SaaS optimization placement problem that not only is applied to initial deployment of SaaS, but also is applied to component dynamic deployment in the running phase of SaaS. A hybrid genetic and simulated annealing algorithm(HGSA) is used in this approach that combines the advantages of genetic algorithm and simulated annealing algorithm, and overcomes the problems of the premature of genetic algorithm and the lower convergence speed. Compared with the separated using of genetic algorithm and simulated annealing algorithm, the experimental results show that HGSA has higher quality in solving the problem of SaaS component optimization placements. The approach proposed in this paper will provide the support of theory and method for the large-scale application of SaaS service mode.

    Reference
    [1] Kang S, Kang S, Hur S. A design of the conceptual architecture for a multitenant SaaS application platform. In:Proc. of the Int'l Conf. on Computers, Networks, Systems, and Industrial Engineering. IEEE Computer Society Press, 2011. 462-467.[doi:10.1109/CNSI.2011.56]
    [2] Zhang Y, Wang ZH, Gao B, Guo CJ, Sun W, Li XP. An effective heuristic for on-line tenant placement problem in SaaS. In:Proc. of the 2010 IEEE Int'l Conf. on Web Services. IEEE Computer Society Press, 2010. 425-432.[doi:10.1109/ICWS.2010.65]
    [3] Yu HY, Wang DH. System resource allocation algorithm for multi-tenant SaaS application. In:Proc of the 2011 Int'l Conf. on Cloud and Service Computing. IEEE Computer Society Press, 2011. 207-211.[doi:10.1109/CSC.2011.6138523]
    [4] Wu LL, Garg SK, Buyya R. SLA-Based admission control for a software-as-a-service provider in cloud computing environments. Journal of Computer and System Sciences, 2012,78(5):1280-1299.[doi:10.1016/j.jcss.2011.12.014]
    [5] Yang EF, Zhang Y, Wu L, Liu YL, Liu SJ. A hybrid approach to placement of tenants for service-based multi-tenant SaaS application. In:Proc. of the 2011 IEEE Asia-Pacific Services Computing Conf. IEEE Computer Society Press, 2011. 124-130.[doi:10.1109/APSCC.2011.35]
    [6] Tian C, Jiang HB, Iyengar A, Liu X, Wu ZD, Chen JH, Liu WY, Wang CG. Improving application placement for cluster-based Web applications. IEEE Trans. on Network and Service Management, 2011,8(2):104-115.[doi:10.1109/TNSM.2011.050311. 100040]
    [7] Yusoh ZIM, Tang M. Composite SaaS placement and resource optimization in cloud computing using evolutionary algorithms. In:Proc. of the 2012 IEEE 5th Int'l Conf. on the Cloud Computing. IEEE Computer Society Press, 2012. 590-597.[doi:10.1109/CLOUD.2012.61]
    [8] Lloyd W, Pallickara S, David O, LyonbAuthor J, ArabibAuthor M, Rojas K. Performance implications of multi-tier application deployments on infrastructure-as-a-service clouds:Towards performance modeling. Future Generation Computer Systems, 2013, 29(5):1254-1264.[doi:10.1016/j.future.2012.12.007]
    [9] Moens H, Truyen E, Walraven S, Joosen W, Dhoedt B, De Turck F. Cost-Effective feature placement of customizable multi-tenant application in the cloud. Journal of Network System Management, 2014,22(4):517-588.[doi:10.1007/s10922-013-9265-5]
    [10] Zhu XY, Santos C, Beyer D, Ward J, Singhal S. Automated application component placement in data centers using mathematical programming. Int'l Journal of Network Management, 2008,18:467-483.[doi:10.1002/nem.707]
    [11] Jin ZH, Cao J, Li ML. A distributed application component placement approach for cloud computing environment. In:Proc. of the 9th IEEE Int'l Conf. on Dependable, Automic and Secure Computing. IEEE Computer Society Press, 2011. 488-495.[doi:10. 1109/DASC.2011.94]
    [12] Sailer A, Head MR, Kochut A, Shaikh H. Graph-Based cloud service placement. In:Proc. of the 2010 IEEE Int'l Conf. on Services Computing. IEEE Computer Society Press, 2010. 89-96.[doi:10.1109/SCC.2010.67]
    [13] Pisinger D. An exact algorithm for large multiple knapsack problem. European Journal of Operational Research, 1999,114(3):528-541.[doi:10.1016/S0377-2217(98)00120-9]
    [14] Kataoka S, Yamada T. Upper and lower bounding procedures for the multiple knapsack assignment problem. European Journal of Operational Research, 2014,237(2):440-447.[doi:10.1016/j.ejor.2014.02.014]
    [15] Sarac T, Sipahioglu A. Generalized quadratic multiple knapsack problem and two solution approaches. Computer & Operations Research, 2014,43:78-89.[doi:10.1016/j.cor.2013.08.018]
    [16] Ren ZG, Feng ZR, Zhang AM. Fusing ant colony optimization with Lagrangian relaxation for the multiple-choice multidimensional knapsack problem. Information Science, 2012,182(1):15-29.[doi:10.1016/j.ins.2011.07.033]
    [17] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ. IEEE Trans. on Evolutionary Computation, 2002,6(2):182-197.[doi:10.1109/4235.996017]
    [18] McCall J. Genetic algorithms for modelling and optimization. Journal of Computational and Applied Mathematics, 2005,184(1):205-222.[doi:10.1016/j.cam.2004.07.034]
    [19] Chen D, Lee CY, Park CH. Hybrid genetic algorithm and simulated annealing(HGASA) in global function optimization. In:Proc. of the 17th IEEE Int'l Conf. on Tools with Artificial Intelligence. IEEE Computer Society Press, 2005. 113-117.[doi:10.1109/ICTAI.2005.72]
    [20] Li WD, Ong SK, Nee AYC. Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int'l Journal of Production Research, 2002,40(8):1899-1922.[doi:10.1080/00207540110119991]
    [21] Loukil L, Mehdi M, Mebab N. A parallel hybrid genetic algorithm-simulated annealing for solving Q3AP on computational grid. In:Proc. of the IEEE Int'l Symp. on Parallel & Distributed Processing. IEEE Computer Society Press, 2009. 1-8.[doi:10.1109/IPDPS. 2009.5161126]
    [22] Li YH, Guo H, Wang L, Fu J. A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under E-supply chain environment. The Scientific World Journal, 2013.[doi:10.1155/2013/125893]
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孟凡超,初佃辉,李克秋,周学权.基于混合遗传模拟退火算法的SaaS构件优化放置.软件学报,2016,27(4):916-932

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History
  • Received:June 30,2015
  • Revised:October 15,2015
  • Online: January 14,2016
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