We propose DISCO - a lightweight approach to flow monitoring in the data plane. The idea is to disaggregate the computation of a single (logically) centralized sketch into multiple small "sketch fragments" that are distributed across the flows' paths. This allows use less resources at switches without trading on telemetry capabilities.

Packet classification is an important part of many networking devices, such as routers and firewalls. Software-defined networking (SDN) heavily relies on online packet classification which must efficiently process two different streams: incoming packets to classify and rules to update. This rules out many offline packet classification algorithms that do not support fast updates. We propose a novel online classification algorithm, TupleMerge (TM), derived from tuple space search (TSS), the packet classifier used by Open vSwitch (OVS). TM improves upon TSS by combining hash tables which contain rules with similar characteristics. This greatly reduces classification time preserving similar performance in updates. We validate the effectiveness of TM using both simulation and deployment in a full-fledged software router, specifically within the vector packet processor (VPP). In our simulation results, which focus solely on the efficiency of the classification algorithm, we demonstrate that TM outperforms all other state of the art methods, including TSS, PartitionSort (PS), and SAX-PAC. For example, TM is 34% faster at classifying packets and 30% faster at updating rules than PS. We then experimentally evaluate TM deployed within the VPP framework comparing TM against linear search and TSS, and also against TSS within the OVS framework. This validation of deployed implementations is important as SDN frameworks have several optimizations such as caches that may minimize the influence of a classification algorithm. Our experimental results clearly validate the effectiveness of TM. VPP TM classifies packets nearly two orders of magnitude faster than VPP TSS and at least one order of magnitude faster than OVS TSS.

This paper presents a method to implement complex tasks into stateful SDN programmable dataplanes. In particular, the presented method proposes to use the internal microcontroller typically used to configure the programmable dataplane also to perform some complex operations that do not require to be executed on each packet. These operations can be executed on a set of data gathered by the dataplane and processed in a time scale that is much higher than the time window of a packet, but is much less than time scale needed for an external SDN controller. Moreover, the use of the configuration microcontroller instead of an external SDN controller to avoid the exchange of data on the control links permits a fine grain tuning of the operations to perform from the timing point of view (precise timestamping, low latency etc.). A set of measurements showing the feasibility of the method are presented, and a simple use case is described to show the effectiveness of this method to enhance the capability of stateful programmable dataplanes.