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.
翻译:资源分列数据中心架构有望在数据中心内远程汇集资源,使日益重要的基础设施服务业务具有更大的灵活性和资源效率。这可以通过在数据中心网络中使用光电转换主干网(DCN)实现;提供所需的带宽和延时保障,以确保非本地资源库应用程序运行时的可靠性能。然而,在这种情景中,资源分配需要同时将服务器一级的网络一级资源分配给请求。这一问题的在线性质和潜在的组合复杂性,加上典型的DCN型结构学规模,使得精确的解决方案不可能实现,而且基于超常的解决方案也不可能在数据中心网络的亚优或非直观设计上;我们证明,如果该政策是采用\emph{线性网络网络的模型,则可以用来进一步学习有效的\emph{网络认知}和/emph{统计学上可调整}以及最终的配置政策。将精确的解决方案和基于超常态的基于超额电路基解决方案的解决方案(在高额网络和高额网络的接受度培训中,可以实现最高级的网络性能和最高级的网络化的接受度),可以与最高级的网络化的网络化资源分配相比,可以实现最高级的网络和最高级的升级的网络化的接受性能,可以实现最高级的接受性能。