Benefited from the design of separating control plane and data plane, software defined networking (SDN) is widely concerned and applied. Its quick response capability to network events with changes in network policies enables more dynamic management of data center networks. Although the SDN controller architecture is increasingly optimized for swift policy updates, the data plane, especially the prevailing ternary content?addressable memory (TCAM) based flow tables on physical SDN switches, remains unoptimized for fast rule updates, and is gradually becoming the primary bottleneck along the policy update pipeline. In this paper, we present RuleTris, the first SDN update optimization framework that minimizes rule update latency for TCAM?based switches. RuleTris employs the dependency graph (DAG) as the key abstraction to minimize the update latency. RuleTris efficiently obtains the DAGs with novel dependency preserving algorithms that incrementally build rule dependency along with the compilation process. Then, in the guidance of the DAG, RuleTris calculates the TCAM update schedules that minimize TCAM entry moves, which are the main cause of TCAM update inefficiency. In evaluation, RuleTris achieves a median of <12 ms and 90?percentile of < 15ms the end?to?end perrule update latency on our hardware prototype, outperforming the state?of?the?art composition compiler CoVisor by [~]20 times.
SDN; SDN?based cloud; network management; access control; unauthorized attack