一种基于简谐振动的云资源分配方法
发布时间：2018-11-05 17:52
【摘要】：为优化云服务系统的资源分配,提高不同资源类型的服务质量,提出基于简谐振动的云资源分配模型,设计一种求解模型的迭代算法.根据谐振子运动特性进行能级划分,加强对邻域内最优解的精细搜索,降低云资源被局部分配的概率,依据能级差构造解空间,使用简谐系统能量转换规律自适应调整解向量的搜索步长.通过实验验证分配模型的求解算法以及解的质量,相比分支定界法和遗传算法相比,该算法在较大规模问题上执行效率高且资源分配成本低.
[Abstract]:In order to optimize the resource allocation of cloud service system and improve the quality of service of different resource types, a cloud resource allocation model based on simple harmonic vibration is proposed, and an iterative algorithm for solving the model is designed. According to the kinematic characteristics of harmonic oscillator, the energy level is partitioned, and the fine search of the optimal solution in the neighborhood is strengthened, the probability of local distribution of cloud resources is reduced, and the solution space is constructed according to the energy level difference. The search step of the solution vector is adaptively adjusted by the energy conversion law of the simple harmonic system. The algorithm and the quality of the solution are verified by experiments. Compared with the branch-and-bound method and the genetic algorithm, the algorithm is more efficient and has lower resource allocation cost than the genetic algorithm.
【作者单位】： 河南城建学院计算机与数据科学学院;
【基金】：国家自然科学基金资助项目(61202248)~~
【分类号】：TP393.09
，
本文编号：2312855
[Abstract]:In order to optimize the resource allocation of cloud service system and improve the quality of service of different resource types, a cloud resource allocation model based on simple harmonic vibration is proposed, and an iterative algorithm for solving the model is designed. According to the kinematic characteristics of harmonic oscillator, the energy level is partitioned, and the fine search of the optimal solution in the neighborhood is strengthened, the probability of local distribution of cloud resources is reduced, and the solution space is constructed according to the energy level difference. The search step of the solution vector is adaptively adjusted by the energy conversion law of the simple harmonic system. The algorithm and the quality of the solution are verified by experiments. Compared with the branch-and-bound method and the genetic algorithm, the algorithm is more efficient and has lower resource allocation cost than the genetic algorithm.
【作者单位】： 河南城建学院计算机与数据科学学院;
【基金】：国家自然科学基金资助项目(61202248)~~
【分类号】：TP393.09
，
本文编号：2312855
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