摘要翻译：
突发争用是光突发交换(OBS)网络中一个众所周知的具有挑战性的问题。争用解决方法总是被动的，并试图基于核心节点上可用的本地信息来最小化BLR。另一方面，在突发损失发生之前避免突发损失的主动方法是可取的。为了降低突发争用的概率，需要一种比最短路径更鲁棒的路由算法。本文提出了一种新的基于JET的光突发交换网络的路由机制，称为图形概率路由模型(GPRM)，它利用贝叶斯网络在逐跳的基础上选择利用率较低的链路。我们假设在OBS网络的核心节点上没有波长转换和缓冲可用。通过在NSFnet网络拓扑结构上使用Network Simulator2(ns-2)并结合实际流量矩阵，在动态负载下对该方法进行了仿真，结果表明，与静态方法相比，该方法降低了突发丢失率(BLR)。仿真结果清楚地表明，该方法在BLR方面优于静态方法。
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英文标题：
《Graphical Probabilistic Routing Model for OBS Networks with Realistic
  Traffic Scenario》
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作者：
Martin Levesque and Halima Elbiaze
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Networking and Internet Architecture        网络和因特网体系结构
分类描述：Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
涵盖计算机通信网络的所有方面，包括网络体系结构和设计、网络协议和网络间标准（如TCP/IP）。还包括与Internet体系结构和性能直接相关的主题，如web缓存。大致包括除C.2.4以外的所有ACM主题类C.2，后者更有可能将分布式、并行和集群计算作为主要主题领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Burst contention is a well-known challenging problem in Optical Burst Switching (OBS) networks. Contention resolution approaches are always reactive and attempt to minimize the BLR based on local information available at the core node. On the other hand, a proactive approach that avoids burst losses before they occur is desirable. To reduce the probability of burst contention, a more robust routing algorithm than the shortest path is needed. This paper proposes a new routing mechanism for JET-based OBS networks, called Graphical Probabilistic Routing Model (GPRM) that selects less utilized links, on a hop-by-hop basis by using a bayesian network. We assume no wavelength conversion and no buffering to be available at the core nodes of the OBS network. We simulate the proposed approach under dynamic load to demonstrate that it reduces the Burst Loss Ratio (BLR) compared to static approaches by using Network Simulator 2 (ns-2) on NSFnet network topology and with realistic traffic matrix. Simulation results clearly show that the proposed approach outperforms static approaches in terms of BLR.
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PDF链接：
https://arxiv.org/pdf/0907.4447