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Network administrators want to detect TCP-level packet reordering to diagnoseperformance problems and attacks. However, reordering is expensive to measure,because each packet must be processed relative to the TCP sequence number ofits predecessor in the same flow. Due to the volume of traffic, detectionshould take place in the data plane as the packets fly by. However,restrictions on the memory size and the number of memory accesses per packetmake it impossible to design an efficient algorithm for pinpointing flows withheavy packet reordering. In practice, packet reordering is typically a propertyof a network path, due to a congested or flaky link. Flows traversing the samepath are correlated in their out-of-orderness, and aggregating out-of-orderstatistics at the IP prefix level provides useful diagnostic information. Inthis paper, we present efficient algorithms for identifying IP prefixes withheavy packet reordering under memory restrictions. First, we sample as manyflows as possible, regardless of their sizes, but only for a short period at atime. Next, we separately monitor the large flows over long periods, inaddition to the flow sampling. In both algorithms, we measure at the flowlevel, and aggregate statistics and allocate memory at the prefix level. Oursimulation experiments, using packet traces from campus and backbone networks,and our P4 prototype show that our algorithms correctly identify $80\%$ of theprefixes with heavy packet reordering using moderate memory resources.

Detecting TCP Packet Reordering in the Data Plane