DISCLAIMER: Described reconf test methodology is experimental, and subject to change following consultation within csit-dev, vpp-dev and user communities. Current test results should be treated as indicative.
Reconf tests are designed to measure the impact of VPP re-configuration on data plane traffic. While VPP takes some measures against the traffic being entirely stopped for a prolonged time, the immediate forwarding rate varies during the re-configuration, as some configurations steps need the active dataplane worker threads to be stopped temporarily.
As the usual methods of measuring throughput need multiple trial measurements with somewhat long durations, and the re-configuration process can also be long, finding an offered load which would result in zero loss during the re-configuration process would be time-consuming.
Instead, reconf tests find a througput value (lower bound for NDR) without re-configuration, and then maintain that ofered load during re-configuration. The measured loss count is then assumed to be caused by the re-configuration process. The result published by reconf tests is the effective blocked time, that is the loss count divided by the offered load.
Each reconf suite is based on a similar MLRsearch performance suite.
MLRsearch parameters are changed to speed up the throughput discovery. For example, PDR is not searched for, and final trial duration is shorter.
The MLRsearch suite has to contain a configuration parameter that can be scaled up, e.g. number of routes or number of service chains. Currently, only increasing the scale is supported as the re-configuration operation. In future, scale decrease or other operations can be implemented.
The traffic profile is not changed, so the traffic present is processed only by the smaller scale configuration. The added routes / chains are not targetted by the traffic.
For the re-configuration, the same Robot Framework and Python libraries are used, as were used in the initial configuration, with the exception of the final calls that do not interact with VPP (e.g. starting virtual machines) being skipped to reduce the test overall duration.
Robot Framework introduces a certain overhead, which may affect timing of individual VPP API calls, which in turn may affect the number of packets lost.
The exact calls executed may contain unnecessary info dumps, repeated commands, or commands which change a value that do not need to be changed (e.g. MTU). Thus, implementation details are affecting the results, even if their effect on the corresponding MLRsearch suite is negligible.
The lower bound for NDR is the only value safe to be used when zero packets lost are expected without re-configuration. But different suites show different “jitter” in that value. For some suites, the lower bound is not tight, allowing full NIC buffers to drain quickly between worker pauses. For other suites, lower bound for NDR still has quite a large probability of non-zero packet loss even without re-configuration.
But the results show very high effective blocked time, so the two objections related to NDR lower bound are negligible in comparison.