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Load Shared Sequential Routing in MPLS Networks: System and User Optimal Solutions

机译:MPLS网络中的负载分担顺序路由:系统和用户最佳解决方案

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摘要

Recently Gerald Ash has shown through case studies that event dependent routing is attractive in large scale multi-service MPLS networks. In this paper, we consider the application of Load Shared Sequential Routing (LSSR) in MPLS networks where the load sharing factors are updated using reinforcement learning techniques. We present algorithms based on learning automata techniques for optimizing the load sharing factors both from the user equilibrium and system optimum perspectives. To overcome the computationally expensive gradient evaluation associated with the Kuhn-Tucker conditions of the system optimum problem, we derive a computationally efficient method employing shadow prices. The proposed method for calculating the user equilibrium solution represents a computationally efficient alternative to discrete event simulation. Numerical results are presented for the performance comparison of the LSSR model with the user equilibrium and the system optimum load sharing factors in some example network topologies and traffic demands.
机译:最近,Gerald Ash通过案例研究表明,事件相关路由在大规模多服务MPLS网络中很有吸引力。在本文中,我们考虑了负载共享顺序路由(LSSR)在MPLS网络中的应用,在该网络中,使用强化学习技术更新了负载共享因子。我们提出了基于学习自动机技术的算法,用于从用户均衡和系统最佳角度优化负载分配因子。为了克服与系统最优问题的Kuhn-Tucker条件相关的计算昂贵的梯度评估,我们推导了一种使用影子价格的计算有效方法。所提出的用于计算用户平衡解的方法代表了离散事件模拟的一种高效计算替代方案。给出了数值结果,用于在某些示例网络拓扑和流量需求中,将LSSR模型与用户均衡和系统最佳负载分担因子进行性能比较。

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