NOX: towards an operating system for networks
ACM SIGCOMM Computer Communication Review
TCAM Razor: a systematic approach towards minimizing packet classifiers in TCAMs
IEEE/ACM Transactions on Networking (TON)
Leveraging Zipf's law for traffic offloading
ACM SIGCOMM Computer Communication Review
Header space analysis: static checking for networks
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Abstractions for network update
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
A safe, efficient update protocol for openflow networks
Proceedings of the first workshop on Hot topics in software defined networks
Real time network policy checking using header space analysis
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Achieving high utilization with software-driven WAN
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
On consistent updates in software defined networks
Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks
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A consistent update installs a new packet-forwarding policy across the switches of a software-defined network in place of an old policy. While doing so, such an update guarantees that every packet entering the network either obeys the old policy or the new one, but not some combination of the two. In this paper, we introduce new algorithms that trade the time required to perform a consistent update against the rule-space overhead required to implement it. We break an update in to k rounds that each transfer part of the traffic to the new configuration. The more rounds used, the slower the update, but the smaller the rule-space overhead. To ensure consistency, our algorithm analyzes the dependencies between rules in the old and new policies to determine which rules to add and remove on each round. In addition, we show how to optimize rule space used by representing the minimization problem as a mixed integer linear program. Moreover, to ensure the largest flows are moved first, while using rule space efficiently, we extend the mixed integer linear program with additional constraints. Our initial experiments show that a 6-round, optimized incremental update decreases rule space overhead from 100% to less than 10%. Moreover, if we cap the maximum rule-space overhead at 5% and assume the traffic flow volume follows Zipf's law, we find that 80% of the traffic may be transferred to the new policy in the first round and 99% in the first 3 rounds.