No abstract

To better provision fast-emerging network applications with various quality-of-service demands, datacenter network (DCN) operators need an effective network orchestration scheme that can coordinate IT and bandwidth resources for differentiated services in a timely manner. In this work, we consider a hybrid optical–electrical DCN (HOE-DCN) and study how to achieve scalable knowledge-defined network orchestration (KD-NO) for managing the delay-sensitive and delay-tolerant applications in it. For delay-sensitive applications, we leverage a multi-agent scheme to distribute the tasks of placing virtual machines (VMs) in server racks and routing VM traffic in electrical–optical inter-rack clouds to two cooperative deep reinforcement learning modules, respectively. Then, we utilize a classic-algorithm-based module to provision delay-tolerant applications with the residual resources in the HOE-DCN. We design the operation and coordination procedure of the KD-NO system and build a small HOE-DCN testbed that consists of four server racks to demonstrate its performance experimentally. Experimental results indicate that our KD-NO system can make timely and correct network orchestration decisions and have better convergence performance compared with the existing benchmark.

No abstract