The Case for Dynamic Bias in Global Adaptive Routing

Abstract

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 - Decoupled, Gradient descent-based Biasing 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.