基于Pareto熵的多目标虚拟网络映射算法  

Multi-objective Virtual Network Mapping Algorithm Based on Pareto Entropy

作　　者：刘颖 王聪 苑迎 蒋国佳 刘珂祯 王翠荣 LIU Ying;WANG Cong;YUAN Ying;JIANG Guojia;LIU Kezhen;WANG Cuirong(College of Computer and Communication Engineering,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066004,China)

机构地区：[1]东北大学秦皇岛分校计算机与通信工程学院,河北秦皇岛066004

出　　处：《计算机工程》2020年第8期153-159,共7页Computer Engineering

基　　金：国家自然科学基金(61702089);中央高校基本科研项目(N182304021);河北省高等学校科学研究计划项目(ZD2019306)。

摘　　要：虚拟网络映射是云资源租赁的关键问题,能够为用户的请求合理地分配底层硬件资源。目前多数研究仅关注物理网络的收益目标,忽视了其能耗问题。为此,结合收益和能耗两个目标,提出一种多目标虚拟网络映射VNE-MOPSO算法。针对该问题,提出一种多目标虚拟网络映射VNE-MOPSO算法。通过引入Pareto熵多目标优化模型,计算两次迭代过程的差熵,评估群体进化情况并将其作为反馈信息设计动态自适应的粒子参数策略,以达到近似最优多目标优化映射方案求解的目的。仿真结果表明,与单目标映射算法相比,该算法的映射成本和能耗更低,且平均收益明显提高。Virtual network mapping is the key problem of cloud resource leasing,which can allocate the underlying hardware resources reasonably for requests of users.At present,most of the researches only focus on the profit goal of physical network and ignore its energy consumption.Therefore,this paper proposes a multi-objective virtual network mapping VNE-MOPSO algorithm combined with revenue and energy consumption.The Pareto entropy-based multi-objective optimization model is introduced to calculate the difference entropy of the two iterations.Then the evolution of population is evaluated,and the evaluation result serves as the feedback to design the dynamic adaptive particle parameter strategy,so as to solve the approximate optimal multi-objective optimization mapping scheme.Simulation results show that compared with the single target mapping algorithm,the mapping cost and energy consumption of the proposed algorithm are lower,and the average benefit is significantly improved.

关 键 词：虚拟网络映射 多目标优化 离散粒子群算法 Pareto熵 能耗 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]