Orca: Server-assisted Multicast for Datacenter Networks

Khaled Diab, Parham Yassini, Mohamed Hefeeda

Abstract: Group communications appear in various large-scale datacenter applications. These applications, however, do not currently benefit from multicast, despite its potential substantial savings in network and processing resources. This is because current multicast systems do not scale and they impose considerable state and communication overheads. We propose a new architecture, called Orca, that addresses the challenges of multicast in datacenter networks. Orca divides the state and tasks of the data plane among switches and servers, and it partially offloads the management of multicast sessions to servers. Orca significantly reduces the state at switches, minimizes the bandwidth overhead, incurs small and constant processing overhead, and does not limit the size of multicast sessions. We implemented Orca in a testbed to demonstrate its performance in terms of throughput, consumption of server resources, packet latency, and the impact of server failures. We also implemented a sample multicast application in our testbed, and showed that Orca can substantially reduce its communication time, through optimizing the data transfer between nodes using multicast instead of unicast. In addition, we simulated a datacenter consisting of 27,648 hosts and handling 1M multicast sessions, and we compared Orca versus the state-of-art system in the literature. Our results show that Orca reduces the switch state by up to two orders of magnitude, the communication overhead by up to 19X, and the control overhead by up to 14X, compared to the state-of-art.