  Resource-disaggregated data centres (RDDC) propose a resource-centric, and
high-utilisation architecture for data centres (DC), avoiding resource
fragmentation and enabling arbitrarily sized resource pools to be allocated to
tasks, rather than server-sized ones. RDDCs typically impose greater demand on
the network, requiring more infrastructure and increasing cost and power, so
new resource allocation algorithms that co-manage both server and networks
resources are essential to ensure that allocation is not bottlenecked by the
network, and that requests can be served successfully with minimal networking
resources. We apply reinforcement learning (RL) to this problem for the first
time and show that an RL policy based on graph neural networks can learn
resource allocation policies end-to-end that outperform previous
hand-engineered heuristics by up to 22.0\%, 42.6\% and 22.6\% for acceptance
ratio, CPU and memory utilisation respectively, maintain performance when
scaled up to RDDC topologies with $10^2\times$ more nodes than those seen
during training and can achieve comparable performance to the best baselines
while using $5.3\times$ less network resources.
