Supervised Learning Based Truthful Auction Mechanism Design in Cloud Computing
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摘要: 使用拍卖方式来进行资源分配可以使得资源提供商获得更大的收益，是云计算领域近年来研究的重点之一。但资源分配问题是NP难的，无法在多项式时间内求解，现有研究主要通过近似算法或启发式算法来实现资源分配，但存在算法耗时长，与最优解相比准确度低的缺点。监督学习中分类及回归思想可对多维云资源分配问题进行建模和分析，针对不同问题规模，该文提出基于线性回归、逻辑回归、支持向量机的3种资源分配算法，并且基于临界值理论设计了支付价格算法，从而确保拍卖机制的可信性。在社会福利、分配准确率、算法执行时间、资源利用率等多个方面进行测试分析，取得了很好的效果。Abstract: Auction based resource allocation can make resource provider get more profit, which is a major challenging problem for cloud computing. However, the resource allocation problem is NP-hard and can not be solved in polynomial time. Existing studies mainly use approximate algorithms or heuristic algorithms to implement resource allocation in auction, but these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, the classification and regression of supervised learning is used to model and analyze multi-dimensional cloud resource allocation, for the different scale of problem, three resource allocation predict algorithms based on linear regression, logistic regression and Support Vector Machine (SVM) are proposed. Through the learning of the small-scale training set, the predict model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to the optimal allocation solution. The payment price algorithm based on the critical value theory is proposed which ensure the truthful property of the auction mechanism design. Final experimental results show that the proposed scheme has good effect for resource allocation in cloud computing.
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Key words:
- Cloud computing /
- Resource allocation /
- Mechanism design /
- Supervised learning
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表 1 基于临界值的价格计算算法(CV-PAY)
输入 所有用户的需求信息集：${R} = \{ R_{}^{(1)}\,R_{}^{(2)}\, ·\!·\!· \,R_{}^{(m)}\} $ 监督学习算法对资源分配的预测结果：
${PD} = ({\rm{pd}}_{}^{(1)}\,{\rm{pd}}_{}^{(2)}\, ·\!·\!· \,{\rm{pd}}_{}^{(m)})$每类虚拟资源的容量：${C} = ({c_1}\;\;\;{c_2}\;\;\; ·\!·\!· \;\;\;{c_n})$ 输出 被选中的用户需要支付的价格，
${Pay} = ({\rm pay}_{}^{(1)}\,{\rm{pay}}_{}^{(2)}\, ·\!·\!· \,{\rm{pay}}_{}^{(m)})$(1) ${{PD}^{\rm{*}}} \leftarrow 0$ (2) $\delta \leftarrow {10^{{\rm{ - }}6}}$ (3) for each $i \leftarrow \{ i|{\rm{pd}}_{}^{(i)} \in {PD}, {\rm{pd}}_{}^{(i)}{\rm{ = 1}}\} $ do (4) ${\rm{pay}}_{}^{(i)} \leftarrow b_{}^{(i)};{\rm{pay}}_{}^{(i)'} \leftarrow 0\;\;\;\;;\;\;\;b_{}^{(i)} \leftarrow ({\rm{pay}}_{}^{(i)} + {\rm{pay}}_{}^{(i)'})/2$ (5) while (${\rm{|pay}}_{}^{(i)}{\rm{ - pay}}_{}^{(i)'}{\rm{| > }}\delta $) do (6) 运行LN-ALLOC, LG-ALLOC或SVM-ALLOC求解
出新的资源分配解${P}{{D}^{\rm{*}}}$(7) if ${\rm{pd}}_{}^{(i)}{\rm{ = }}1\;\;\;,{\rm{pd}}_{}^{(i)} \in {P}{{D}^{\rm{*}}}$ (8) ${\rm{pay}}_{}^{(i)} \leftarrow b_{}^{(i)};b_{}^{(i)} \leftarrow ({\rm{pay}}_{}^{(i)} + {\rm{pay}}_{}^{(i)'})/2$ (9) else (10) ${\rm{pay}}_{}^{(i)'} \leftarrow b_{}^{(i)};b_{}^{(i)} \leftarrow ({\rm{pay}}_{}^{(i)} + {\rm{pay}}_{}^{(i)'})/2$ (11) end if (12) end while (13) ${Pay} \leftarrow {Pay} \cup {\rm{pay}}_{}^{(i)}$ (14) end for (15) return ${Pay}$ 
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