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)
目前,对于SaaS优化放置问题的研究都是假定云环境中的虚拟机的种类和数量都是确定的,即,在限定的资源范围内进行优化.然而,在公有云环境下,SaaS提供者所需要的云资源数量是不确定的,其需要根据IaaS提供者所提供的虚拟机种类以及被部署的SaaS构件的资源需求来确定.为此,站在SaaS提供者角度,提出一种新的SaaS构件优化放置问题模型,并采用混合遗传模拟退火算法(hybrid genetic and simulated annealing algorithm,简称HGSA)对该问题进行求解.HGSA结合了遗传算法和模拟退火算法的优点,克服了遗传算法收敛速度慢和模拟退火算法容易陷入局部最优的缺点,与单独使用遗传算法和模拟退火算法相比,实验结果表明,HGSA在求解SaaS构件优化放置问题方面具有更高的求解质量.所提出的方法为SaaS服务模式的大规模应用提供了理论与方法的支撑.
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.