Abstract
High performance switches employ extremely low latency memory subsystems in an effort to reap the lowest feasible end-to-end flow level latencies. Their capacities are extremely valuable as the size of these memories is limited due to several architectural constraints such as power and silicon area. This necessity is further exacerbated with the emergence of Software Defined Networks (SDN) where fine-grained flow definitions lead to explosion in the number of flow entries. In this paper, we propose FlowMaster, a speculative mechanism to update the flow table by predicting when an entry becomes stale and evict the same early to accommodate new entries. We collage the observations from predictors into a Markov based learning predictor that predicts whether a flow is valuable any more. Our experiments confirm that FlowMaster enables efficient usage of flow tables thereby reducing the discard rate from flow table by orders of magnitude and in some cases, eliminating discards completely.
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Kannan, K., Banerjee, S. (2014). FlowMaster: Early Eviction of Dead Flow on SDN Switches. In: Chatterjee, M., Cao, Jn., Kothapalli, K., Rajsbaum, S. (eds) Distributed Computing and Networking. ICDCN 2014. Lecture Notes in Computer Science, vol 8314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45249-9_32
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DOI: https://doi.org/10.1007/978-3-642-45249-9_32
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