波长路由光网状网络中混合服务水平协议下的共享风险链路组(SRLG)多样化路径配置:制定和解决方法

Lu Shen, Xi Yang, B. Ramamurthy
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引用次数: 22

摘要

波长路由光网络中的静态分配问题已经研究了很多年。但是,服务提供商仍然面临着由于在光层提供服务的特殊要求而产生的挑战。本文将一些现实约束引入到静态供应问题中,并在不同的网络资源可用性条件下给出了静态供应问题的公式。我们考虑了三种不同类型的共享风险链路组(SRLG)路径保护方案:专用、共享和无保护。我们将每个连接请求关联为光路长度约束和收益值。当网络资源不足以容纳所有的连接请求时,静态供应问题被表述为收益最大化问题,其目标是使总收益值最大化。当网络有足够的资源时,这个问题就变成了一个容量最小化问题,其目标是最小化所使用的波长链路的数量。我们给出了这些问题的整数线性规划(ILP)表达式。由于解决这些ILP问题非常耗时,我们提出了一种禁忌搜索启发式算法来在合理的时间内解决这些问题。实验结果比较了ILP求解器、禁忌搜索启发式算法和分治贪婪启发式算法的求解结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shared-risk link group (SRLG)-diverse path provisioning under hybrid service level agreements in wavelength-routed optical mesh networks: formulation and solution approaches
The static provisioning problem in wavelength-routed optical networks has been studied for many years. However, service providers are still facing the challenges arising from the special requirements for provisioning services at the optical layer. In this paper, we incorporate some realistic constraints into the static provisioning problem, and formulate it under different network resource availability conditions. We consider three classes of shared risk link group (SRLG)-diverse path protection schemes: dedicated, shared, and unprotected. We associate with each connection request a lightpath length constraint and a revenue value. When the network resources are not sufficient to accommodate all the connection requests, the static provisioning problem is formulated as a revenue maximization problem, whose objective is maximizing the total revenue value. When the network has sufficient resources, the problem becomes a capacity minimization problem with the objective of minimizing the number of used wavelength-links. We give integer linear programming (ILP) formulations for these problems. Because solving these ILP problems is extremely time consuming, we propose a tabu search heuristic to solve these problems within a reasonable time. Experimental results are presented to compare the solutions obtained by an ILP solver, the tabu search heuristic and a divide-and-conquer greedy heuristic.
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