针对CABO(Concurrent Architectures are Better Than One)网络体系结构下虚拟网内部路由协议可定制的特点设计了流量分配路由算法,对于运营带宽敏感业务的虚拟网络采用路由算法解决流量分配问题,提出一种新的指定路由机制,利用改进的...针对CABO(Concurrent Architectures are Better Than One)网络体系结构下虚拟网内部路由协议可定制的特点设计了流量分配路由算法,对于运营带宽敏感业务的虚拟网络采用路由算法解决流量分配问题,提出一种新的指定路由机制,利用改进的多商品流问题作为流量分配路由算法,以流量均衡、收益最大为分配目标;仿真实验结果表明,采用基于指定路由的流量分配路由机制较传统负载均衡路由算法获得的链路利用率更高、网络性能(丢包率、延迟)更好,并可接受更多的业务请求接入.展开更多
Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of method...Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of methods have been proposed for solving it. However, most researchers only discuss the properties of different models and algorithms without taking into account the impacts of actual implementation. In fact, the true performance of a method may differ greatly across various implementations. In this paper, several popular optimization solvers for implementations of column generation and Lagrangian relaxation are discussed. In order to test scalability and optimality, three groups of networks with different structures are used as case studies. Results show that column generation outperforms Lagrangian relaxation in most instances, but the latter is better suited to networks with a large number of commodities.展开更多
文摘针对CABO(Concurrent Architectures are Better Than One)网络体系结构下虚拟网内部路由协议可定制的特点设计了流量分配路由算法,对于运营带宽敏感业务的虚拟网络采用路由算法解决流量分配问题,提出一种新的指定路由机制,利用改进的多商品流问题作为流量分配路由算法,以流量均衡、收益最大为分配目标;仿真实验结果表明,采用基于指定路由的流量分配路由机制较传统负载均衡路由算法获得的链路利用率更高、网络性能(丢包率、延迟)更好,并可接受更多的业务请求接入.
基金supported by research funds from the National Natural Science Foundation of China (Nos. 61521091, 61650110516, 61601013)
文摘Multi-commodity flow problems（MCFs） can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of methods have been proposed for solving it. However, most researchers only discuss the properties of different models and algorithms without taking into account the impacts of actual implementation. In fact, the true performance of a method may differ greatly across various implementations. In this paper, several popular optimization solvers for implementations of column generation and Lagrangian relaxation are discussed. In order to test scalability and optimality, three groups of networks with different structures are used as case studies. Results show that column generation outperforms Lagrangian relaxation in most instances, but the latter is better suited to networks with a large number of commodities.
