Machine Learning Based Resource Allocation of Cloud Computing in Auction

• 作者: Deepesh Kumar Jixian Zhang Kun Yue Ning Xie Weidong Li Xuejie Zhang
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Cloud COMPUTING resource ALLOCATION machine learning linear regression logistic regression
• 年，卷（期）: 2018,（7）
• 页码范围: 123-135
• 页数: 13页
• 分类号: TP3

摘要

Resource allocation in auctions is a challenging problem for cloud computing.However,the resource allocation problem is NP-hard and cannot be solved in polynomial time.The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution;however,these algorithms have the disadvantages of low computational efficiency or low allocate accuracy.In this paper,we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions.By learning a small-scale training set,the prediction model can guarantee that the social welfare,allocation accuracy,and resource utilization in the feasible solution are very close to those of the optimal allocation solution.The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.