具有高量子计算就绪度的队列感知网络控制算法——在胖管道网络离散时间流模拟器中评估
Queue-aware Network Control Algorithm with a High Quantum Computing Readiness-Evaluated in Discrete-time Flow Simulator for Fat-Pipe Networks
AI总结：
提出一种可在量子退火计算机上运行的队列感知、基于 ILP 的资源重占用网络控制算法，并用离散时间流模拟器验证其可将突发流量丢失降低一半。
摘要：
量子计算这一新兴技术有可能改变未来问题的求解方式。本工作提出一种集中式网络控制算法，可在已有的、基于量子退火原理的量子计算机上执行，例如 D-Wave Advantage。我们引入了一种用于广域网流量工程的资源重占用算法。所提出的优化算法在收发器过载时改变流量引导和资源分配。出于稳定性考虑，光纤放大器和收发器等有源组件的设置不会被更改。该算法适用于网络流量在秒级时间尺度上波动或发生突发的情况。此外，我们开发了一个离散时间流模拟器，用于研究该算法在广域网中的性能。我们的网络模拟器考虑了带缓冲传输线路的积压和丢失建模。在出现积压时，并发流将得到平等处理。本工作提供了一种基于 ILP 的网络配置算法，可应用于量子退火计算机。我们展示了，在具有突发流量的网络中应用资源重占用算法时，流量丢失可显著降低至原来的一半，即减少 2 倍。由于在高负载情况下通过重占用可更高效地使用资源，因此可以减少网络的过度配置。由此，这种新型网络运行方式推动网络走向零余量网络。我们表明，新引入的网络模拟器能够分析胖管道网络内的短时效应，例如缓冲。由于真实规模网络中网络配置的计算通常非常耗时，量子计算可使所提出的网络配置算法应用于真实规模的广域网。
英文摘要：
The emerging technology of quantum computing has the potential to change the way how problems will be solved in the future. This work presents a centralized network control algorithm executable on already existing quantum computer which are based on the principle of quantum annealing like the D-Wave Advantage. We introduce a resource reoccupation algorithm for traffic engineering in wide-area networks. The proposed optimization algorithm changes traffic steering and resource allocation in case of overloaded transceivers. Settings of active components like fiber amplifiers and transceivers are not changed for the reason of stability. This algorithm is beneficial in situations when the network traffic is fluctuating in time scales of seconds or spontaneous bursts occur. Further, we developed a discrete-time flow simulator to study the algorithm's performance in wide-area networks. Our network simulator considers backlog and loss modeling of buffered transmission lines. Concurring flows are handled equally in case of a backlog. This work provides an ILP-based network configuring algorithm that is applicable on quantum annealing computers. We showcase, that traffic losses can be reduced significantly by a factor of 2 if a resource reoccupation algorithm is applied in a network with bursty traffic. As resources are used more efficiently by reoccupation in heavy load situations, overprovisioning of networks can be reduced. Thus, this new form of network operation leads toward a zero-margin network. We show that our newly introduced network simulator enables analyses of short-time effects like buffering within fat-pipe networks. As the calculation of network configurations in real-sized networks is typically time-consuming, quantum computing can enable the proposed network configuration algorithm for application in real-sized wide-area networks.
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