本文已被：浏览 1997次 下载 2232次
Received:June 26, 2018 Revised:July 20, 2018
Received:June 26, 2018 Revised:July 20, 2018
中文摘要: 针对云资源提供问题，为了降低云消费者的资源使用成本，提出了一种采用随机规划模型的云资源分配算法.同时考虑按需实例和预留实例，采用两阶段随机整数规划对云资源提供问题进行建模，在资源预留阶段，根据长期的工作负载情况，确定预留实例的类型和数量，在按需分配阶段，根据当前的工作负载，确定动态分配的按需实例的类型和数量.采用抽样平均近似方法减少资源提供问题的场景数量，降低求解复杂度，并提出了一种基于阶段分解的混合进化算法求解资源提供问题.仿真实验结果表明，采用随机规划模型的云资源分配算法能够在较短时间内获得近似最优的云资源预留方案，有效降低了云消费者的资源使用成本.
Abstract:Aiming at the cloud resource provisioning problem, a stochastic programming based cloud resource allocation algorithm is proposed to reduce the total resource provisioning cost of the service provider. Combining the reservation plan and the on-demand plan, the cloud resource provisioning problem can be formulated as a two-stage stochastic programming problem using the probability distribution of the workload demand. Then the sample average approximation approach and a stage decomposition based hybrid evolutionary algorithm are applied to solve the cloud resource provisioning problem. The simulation results show that the proposed cloud resource allocation algorithm can obtain near optimal resource reservation scheme within a short time, reducing the total resource provisioning cost of the service provider significantly.
keywords: cloud resource allocation reservation stochastic programming sample average approximation hybrid evolutionary algorithm
文章编号： 中图分类号： 文献标志码：
基金项目:江苏省高校自然科学研究资助项目（15KJD520002）
| Author Name | Affiliation | |
| CHEN Jun-Jie | School of Electronics and Information, Nantong University, Nantong 226019, China | cjjcy@ntu.edu.cn |
| Author Name | Affiliation | |
| CHEN Jun-Jie | School of Electronics and Information, Nantong University, Nantong 226019, China | cjjcy@ntu.edu.cn |
引用文本：
陈俊杰.采用随机规划模型的云资源分配算法.计算机系统应用,2019,28(2):140-145
CHEN Jun-Jie.Cloud Resource Allocation Algorithm Using Stochastic Programming.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):140-145
陈俊杰.采用随机规划模型的云资源分配算法.计算机系统应用,2019,28(2):140-145
CHEN Jun-Jie.Cloud Resource Allocation Algorithm Using Stochastic Programming.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):140-145
