WDM网络中的随机RWA与光路重路由
Stochastic RWA and Lightpath Rerouting in WDM Networks
AI总结：
针对WDM网络动态流量下的RWA与光路重路由问题，提出首个两阶段随机整数规划模型及分解求解方法，模拟显示服务等级提升19%、频谱节省20%、请求授予率提升36%。
摘要：
在电信网络中，路由与波长分配（RWA）是为传入的连接请求寻找光路的问题。面对动态流量时，基于预定义的确定性策略对传入请求进行贪婪的光路分配会导致网络碎片化，由于带宽搁浅而无法充分利用其全部容量。此时，服务提供商试图通过碎片整理过程来恢复容量。我们从两个角度研究这一设置：（i）通过RWA问题授予连接请求时，以及（ii）在通过光路重路由进行碎片整理的过程中。对于这两个问题，我们提出了第一个两阶段随机整数规划模型，该模型结合了传入请求的不确定性，以最大化期望的服务等级。我们开发了一种基于分解的求解方法，该方法使用了问题的各种松弛以及一种新开发的特定于问题的割族。在52个阶段的滚动时域框架中对各种实例的两阶段策略进行模拟表明，与传统的确定性策略相比，我们的随机模型提供了高质量的解决方案。具体来说，所提出的配置策略在整体服务等级上提高了高达19%，在频谱节省上提高了20%，而随机光路重路由策略在仅使用最多4%额外带宽频谱的情况下，授予了多达36%更多的请求。
英文摘要：
In a telecommunication network, Routing and Wavelength Assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity due to stranded bandwidth. At this point service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions compared to traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests using up to just 4% more bandwidth spectrum.
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