As an aside, I worked on path tracing[1] long ago, and there was various schemes to avoid the "clumpyness" you'd get by using purely random numbers. This inclu

As an aside, I worked on path tracing[1] long ago, and there was various schemes to avoid the "clumpyness" you'd get by using purely random numbers. This included regular stratified sampling[2] as well as using quasi-random low-discrepancy sequences[3] like the Sobol sequence[4], which would "space out" the random samples. Probably not terribly relevant for load balancing, but I did find the quasi-random stuff fascinating. How to make something "random enough" without being "too random". edit: Though now I got a shower thought moment, could stratified sampling be used as a variant of the "two choices", even if each load-balancer worked independently? That is, each load-balancer stratifies the resources, and keeps a separate index to the current bin. Then for each request pick a random/suitable resource from the current bin and increment the bin index, wrapping when needed. Hmm... [1]: https://en.wikipedia.org/wiki/Path_tracing https://en.wikipedia.org/wiki/Path_tracing [2]: https://en.wikipedia.org/wiki/Stratified_sampling https://en.wikipedia.org/wiki/Stratified_sampling [3]: https://en.wikipedia.org/wiki/Low-discrepancy_sequence https://en.wikipedia.org/wiki/Low-discrepancy_sequence [4]: https://en.wikipedia.org/wiki/Sobol_sequence https://en.wikipedia.org/wiki/Sobol_sequence