  Resource-disaggregated data centre architectures promise a means of pooling
resources remotely within data centres, allowing for both more flexibility and
resource efficiency underlying the increasingly important
infrastructure-as-a-service business. This can be accomplished by means of
using an optically circuit switched backbone in the data centre network (DCN);
providing the required bandwidth and latency guarantees to ensure reliable
performance when applications are run across non-local resource pools. However,
resource allocation in this scenario requires both server-level \emph{and}
network-level resource to be co-allocated to requests. The online nature and
underlying combinatorial complexity of this problem, alongside the typical
scale of DCN topologies, makes exact solutions impossible and heuristic based
solutions sub-optimal or non-intuitive to design. We demonstrate that
\emph{deep reinforcement learning}, where the policy is modelled by a
\emph{graph neural network} can be used to learn effective \emph{network-aware}
and \emph{topologically-scalable} allocation policies end-to-end. Compared to
state-of-the-art heuristics for network-aware resource allocation, the method
achieves up to $20\%$ higher acceptance ratio; can achieve the same acceptance
ratio as the best performing heuristic with $3\times$ less networking resources
available and can maintain all-around performance when directly applied (with
no further training) to DCN topologies with $10^2\times$ more servers than the
topologies seen during training.
