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    面向SD-DCN的OpenFlow分组转发能效联合优化模型

    Joint Optimization Model of Energy Consumption and Efficiency Regarding OpenFlow-Based Packet Forwarding in SD-DCN

    • 摘要: 在软件定义网络(software-defined networking, SDN)中,OpenFlow交换机通常采用三态内容可寻址存储器(ternary content addressable memory, TCAM)存储流表,以支持快速通配查找. 然而,TCAM采用并行查找方式,查找能耗高,因此有必要为OpenFlow交换机选择合适的TCAM容量,以平衡分组转发时延和能耗. 针对软件定义数据中心网络(software-defined data center network, SD-DCN)这一典型应用场景,利用多优先级M/G/1排队模型刻画OpenFlow交换机的分组处理过程,进而建立OpenFlow分组转发时延模型. 同时,基于网络流分布特性,建立TCAM流表命中率模型,以求解OpenFlow分组转发时延与TCAM容量的关系式. 在此基础上,结合TCAM查找能耗,建立OpenFlow分组转发能效联合优化模型,并设计优化算法求解TCAM最优容量. 实验结果表明:所提时延模型比现有模型更能准确刻画OpenFlow分组转发时延. 同时,利用优化算法求解不同参数配置下的TCAM最优容量,为SD-DCN实际部署提供参考依据.

       

      Abstract: In software-defined networking (SDN), OpenFlow switches typically utilize ternary content addressable memory (TCAM) to store flow tables for fast wildcarding lookups. In order to promote packet forwarding performance, it usually requires enlarging TCAM capacity to store more entries. However, TCAM performs lookups in parallel matching, which brings about high energy consumption. Therefore, it is necessary to choose the appropriate TCAM capacity to balance the delay and energy consumption of packet forwarding. For the typical scenario of software-defined data center network (SD-DCN), we characterize the packet processing of an OpenFlow switch as a multi-priority M/G/1 queueing model, and build an OpenFlow-based packet forwarding delay model. Meanwhile, we establish a hit rate model of TCAM flow tables based on flow distribution characteristics, to solve the relational expression between packet forwarding delay and TCAM capacity. Considering the energy consumption of TCAM lookups, we establish a joint optimization model of energy consumption and efficiency regarding packet forwarding, and design an optimization algorithm to solve the optimal TCAM capacity. The experimental results indicate that our proposed delay model can more accurately characterize OpenFlow-based packet forwarding delay than existing models do. Meanwhile, we leverage the optimization algorithm to solve the optimal TCAM capacity with different parameter configurations, which provides a guideline for actual SD-DCN deployments.

       

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