基于改进北极熊算法的多租户数据中心电力成本优化方法研究

Research on power cost optimization method of multi-tenant colocation data center based on improved polar bear optimization

为了满足电力需求响应,通过改进北极熊算法提出了一种面向多租户数据中心的成本优化算法。首先采用一对多的逆向拍卖模型建立租户及运营商的关系,激励租户主动参与拍卖并提交相应的节能方案和期望奖励;接着通过改进现有的北极熊算法,来求解最优租户组合和最小成本,其中采用了sigmoid函数对坐标进行离散化处理;同时为了增强算法跳出局部最优的能力在算法中融入了变异策略,并且为提高算法的寻优能力,采用自适应视野代替固定视野来动态调整局部搜索的范围,进一步降低算法陷入局部最优解的概率。最后将提出的方法分别与经典的算法进行比较,实验结果表明在满足需求响应的情况下,提出的方法不仅花费成本最低,而且具有更高的效率。

With the aim of satisfaction of demand response, this paper proposed an improved polar bear optimization based approach for cost optimization on multi-tenant colocation data centers. This paper used a one-to-many reverse auction model to reveal the relationship between tenants and operator, and encouraged tenants to voluntarily participate in the auction and submit their energy-saving plans and expected rewards. Then, this algorithm adopted the improved polar bear algorithm to solve the optimal tenant combination and minimize the cost. In detail, this method leveraged the sigmoid function to process the discretize problem. Meanwhile, to enhance the ability of jumping out of the local optimum, it integrated a mutation strategy into the algorithm. To further improve the searching ability in a global view, the method designed an adaptive field of view strategy instead of fixed field of view to dynamically adjust the scope of the local search, and reduced the probability of the algorithm trapping into the local optimal solution. Finally, the paper compared the proposed algorithm with several classical algorithms. The experimental results show that the proposed algorithm not only gains the least cost alone, but also improves the efficiency.