Global adaptive routing is a critical component of high-radix networks in large-scale systems and is necessary to fully exploit the path diversity in high-radix topologies. The routing decision in global adaptive routing is made between minimal and non-minimal paths, often based on local information (e.g., queue occupancy) and rely on “approximate” congestion information through backpressure. Different heuristic-based adaptive routing algorithms have been proposed for high-radix topologies but they often rely on local-only information that can lead to inefficient routing decisions. In this letter, we propose DGB – De coupled, G radient descent-based B iasing routing algorithm to address the limitation of global adaptive routing. With DGB, both the local and global congestion information are decoupled in the routing decision. In particular, we propose to leverage a dynamic bias in the global adaptive routing where gradient descent approach is leveraged to adjust the adaptive routing bias appropriately. Our results show that DGB can effectively adjust the bias dynamically to outperform previously proposed routing algorithms on high-radix topologies.

中文翻译：

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全局自适应路由中的动态偏差案例

全局自适应路由是大规模系统中高基数网络的关键组成部分，对于充分利用高基数拓扑中的路径分集是必不可少的。全局自适应路由中的路由决策通常基于本地信息（例如队列占用率）在最小路径与非最小路径之间做出，并依赖于通过背压产生的“近似”拥塞信息。对于高基数拓扑，已经提出了不同的基于启发式的自适应路由算法，但是它们通常依赖于仅本地的信息，这可能导致无效的路由决策。在这封信中，我们建议DGB –德 耦合， G 基于辐射 乙 路由算法解决了全局自适应路由的局限性。使用DGB，本地和全局拥塞信息都可以解耦的在路由决策中。特别是，我们建议利用动态的全局自适应路由中的“偏差”，其中利用梯度下降方法来适当地调整自适应路由偏差。我们的结果表明，DGB可以有效地动态调整偏置，以胜过先前在高基数拓扑上提出的路由算法。