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+---
+title: "Reassembly"
+weight: 10
+---
+
+# Packet reassembly performance
+
+IP protocols (mainly IPv4) specify conditions for packet fragmentation
+and packet reassembly. For VPP, the reassembly operation is more CPU intensive.
+By default, VPP avoids unnecessary work, so there are only few scenarios
+where VPP fragments IP packets, and even less scenarios where it reassemblies
+the fragmented packets.
+
+The typical situation when fragmentation is performed occurs with
+tunnel encapsulation protocols, when the packet after encapsulation
+would not fit into interface MTU (maximum transmission unit).
+Some, but not all, encapsulation protocols also require
+packet reassembly for decapsulation.
+
+As the search algorithms used in CSIT work best when the number of packets
+coming from TG (traffic generator) is the same
+as the number of packets expected to come back to TG,
+the easiest way to test reassembly performance of VPP is using
+a 3-node testbed and a tunneling test suite adapted to cause fragmentation.
+
+## MTU
+
+By default, testbeds in CSIT are configured with MTU high enough
+for encapsulated packets to fit in.
+Not all devices and drivers used by VPP do support lowering MTU value.
+For reassembly tests, only the physical interfaces on the DUT1-DUT2 link
+have lowered MTU, and that currently works only with dpdk plugin.
+
+## Impacts
+
+Reassembly suites with small number of flows and tunnels
+usually place encapsulation+fragmentation and reassembly+decapsulation
+on different workers, so the bottleneck seen in performance results
+is not affected by fragmentation performance.
+
+Reassembly suites with high number of flows and tunnels
+achieve balanced load on all workers, so their overall performance
+is affected by both fragmentation and reassembly performance.
+
+Some protocols (e.g. IPsec) are CPU intensive not only
+on fragmentation and reassembly, but also on encapsulation and decapsulation.
+Reassembly (and depending on scale also fragmentation) impact
+on those tests can still be visible, at least for big regressions.