Achieving minimum bandwidth guarantees and work-conservation in large-scale, SDN-based datacenter networks


Performance interference has been a well-known problem in datacenters and one that remains a constant topic of discussion in the literature. Software-Defined Networking (SDN) may enable the development of a robust solution for interference, as it allows dynamic control over resources through programmable interfaces and flow-based management. However, to date, the scalability of existing SDN-based approaches is limited, because of the number of entries required in flow tables and delays introduced. In this paper, we propose Predictor, a scheme to scalably address performance interference in SDN-based datacenter networks (DCNs), providing minimum bandwidth guarantees for applications and work-conservation for providers. Two novel SDN-based algorithms are proposed to address performance interference. Scalability is improved in Predictor as follows: first, it minimizes flow table size by controlling flows at application-level; second, it reduces flow setup time by proactively installing rules in switches. We conducted an extensive evaluation, in which we verify that Predictor provides (i) guaranteed and predictable network performance for applications and their tenants; (ii) work-conserving sharing for providers; and (iii) significant improvements over DevoFlow (the state-of-the-art SDN-based proposal for DCNs), reducing flow table size (up to 94%) and having similar controller load and flow setup time.

Computer Networks Journal (COMNET)
Daniel Marcon
UFRGS MSc 2011-2013, UFRGS PhD 2013-2017